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train · 44.6k rows

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28c2f34c-6497-429a-8e6b-fa42c6e64885	an-active-learning-approach-for-reducing	1909.02344	null	https://arxiv.org/abs/1909.02344v1	https://arxiv.org/pdf/1909.02344v1.pdf	An Active Learning Approach for Reducing Annotation Cost in Skin Lesion Analysis	Automated skin lesion analysis is very crucial in clinical practice, as skin cancer is among the most common human malignancy. Existing approaches with deep learning have achieved remarkable performance on this challenging task, however, heavily relying on large-scale labelled datasets. In this paper, we present a novel active learning framework for cost-effective skin lesion analysis. The goal is to effectively select and utilize much fewer labelled samples, while the network can still achieve state-of-the-art performance. Our sample selection criteria complementarily consider both informativeness and representativeness, derived from decoupled aspects of measuring model certainty and covering sample diversity. To make wise use of the selected samples, we further design a simple yet effective strategy to aggregate intra-class images in pixel space, as a new form of data augmentation. We validate our proposed method on data of ISIC 2017 Skin Lesion Classification Challenge for two tasks. Using only up to 50% of samples, our approach can achieve state-of-the-art performances on both tasks, which are comparable or exceeding the accuracies with full-data training, and outperform other well-known active learning methods by a large margin.	['Pheng-Ann Heng', 'Cheng Xue', 'Qi Dou', 'Hao Chen', 'Xueying Shi', 'Jing Qin']	2019-09-05	null	null	null	null	['skin-lesion-classification']	['medical']	[ 8.01236928e-01 1.70694679e-01 -5.92214406e-01 -2.83621401e-01 -1.52455056e+00 -2.73167074e-01 6.78355098e-01 5.97080529e-01 -7.96833634e-01 7.71711051e-01 -3.67157646e-02 -8.02636053e-03 -2.57386386e-01 -6.62271738e-01 -3.22585016e-01 -1.16037560e+00 1.78527802e-01 2.37409845e-01 3.51857275e-01 2.07275093e-01 9.36307162e-02 5.21325946e-01 -1.51263583e+00 4.03376669e-01 1.26394415e+00 1.15447211e+00 -1.09243087e-01 6.40017152e-01 -1.62967682e-01 7.18945384e-01 -2.51749188e-01 -5.16641319e-01 8.26238617e-02 -2.37471238e-01 -8.35784674e-01 3.67889434e-01 4.65923935e-01 -7.87274912e-02 9.82303843e-02 8.90049458e-01 8.27244699e-01 -1.95445791e-01 7.67857313e-01 -8.50607395e-01 -1.72338828e-01 4.10415143e-01 -5.00724137e-01 -9.39425826e-02 -2.06800317e-03 3.65054786e-01 9.38637555e-01 -7.76946902e-01 5.36275327e-01 5.17468512e-01 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85e338fa-ffb8-4060-8e7e-f05269ce573b	attention-wise-masked-graph-contrastive	2206.08262	null	https://arxiv.org/abs/2206.08262v1	https://arxiv.org/pdf/2206.08262v1.pdf	Attention-wise masked graph contrastive learning for predicting molecular property	Accurate and efficient prediction of the molecular properties of drugs is one of the fundamental problems in drug research and development. Recent advancements in representation learning have been shown to greatly improve the performance of molecular property prediction. However, due to limited labeled data, supervised learning-based molecular representation algorithms can only search limited chemical space, which results in poor generalizability. In this work, we proposed a self-supervised representation learning framework for large-scale unlabeled molecules. We developed a novel molecular graph augmentation strategy, referred to as attention-wise graph mask, to generate challenging positive sample for contrastive learning. We adopted the graph attention network (GAT) as the molecular graph encoder, and leveraged the learned attention scores as masking guidance to generate molecular augmentation graphs. By minimization of the contrastive loss between original graph and masked graph, our model can capture important molecular structure and higher-order semantic information. Extensive experiments showed that our attention-wise graph mask contrastive learning exhibit state-of-the-art performance in a couple of downstream molecular property prediction tasks.	['Lei Deng', 'Xuejun Liu', 'Yibiao Huang', 'Hui Liu']	2022-05-02	null	null	null	null	['molecular-property-prediction']	['miscellaneous']	[ 7.12812543e-01 8.86081532e-02 -5.38965225e-01 -3.81239384e-01 -6.53690040e-01 -4.57958281e-01 2.45470628e-01 6.15581870e-01 3.09128105e-03 1.15640593e+00 3.88518497e-02 -5.99498749e-01 -2.94206906e-02 -8.95724416e-01 -9.60642278e-01 -8.15625370e-01 -1.38731897e-01 1.78881496e-01 -1.96734786e-01 -7.16614872e-02 2.62469292e-01 6.15327835e-01 -9.32276547e-01 2.04122841e-01 1.32118058e+00 6.57203615e-01 2.89397389e-01 2.18302935e-01 -1.58919603e-01 1.00840330e+00 -2.89130867e-01 -3.79269361e-01 -2.47978959e-02 -7.64518619e-01 -7.31034279e-01 -1.12821914e-01 3.24828744e-01 1.53263789e-02 -5.48644245e-01 1.11048436e+00 6.81065440e-01 3.33973199e-01 7.94437230e-01 -8.53340209e-01 -1.18651056e+00 4.87091899e-01 -6.03135228e-01 2.26646617e-01 4.58752543e-01 2.07519308e-01 1.17828572e+00 -8.55410159e-01 7.02873528e-01 1.07811511e+00 3.21057647e-01 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c66ac54e-53a6-4475-9c46-95119f1416e9	augdmc-data-augmentation-guided-deep-multiple	2306.13023	null	https://arxiv.org/abs/2306.13023v1	https://arxiv.org/pdf/2306.13023v1.pdf	AugDMC: Data Augmentation Guided Deep Multiple Clustering	Clustering aims to group similar objects together while separating dissimilar ones apart. Thereafter, structures hidden in data can be identified to help understand data in an unsupervised manner. Traditional clustering methods such as k-means provide only a single clustering for one data set. Deep clustering methods such as auto-encoder based clustering methods have shown a better performance, but still provide a single clustering. However, a given dataset might have multiple clustering structures and each represents a unique perspective of the data. Therefore, some multiple clustering methods have been developed to discover multiple independent structures hidden in data. Although deep multiple clustering methods provide better performance, how to efficiently capture the alternative perspectives in data is still a problem. In this paper, we propose AugDMC, a novel data Augmentation guided Deep Multiple Clustering method, to tackle the challenge. Specifically, AugDMC leverages data augmentations to automatically extract features related to a certain aspect of the data using a self-supervised prototype-based representation learning, where different aspects of the data can be preserved under different data augmentations. Moreover, a stable optimization strategy is proposed to alleviate the unstable problem from different augmentations. Thereafter, multiple clusterings based on different aspects of the data can be obtained. Experimental results on three real-world datasets compared with state-of-the-art methods validate the effectiveness of the proposed method.	['Juhua Hu', 'Maham Rashid', 'Enbei Liu', 'Jiawei Yao']	2023-06-22	null	null	null	null	['clustering', 'deep-clustering', 'deep-clustering']	['methodology', 'miscellaneous', 'natural-language-processing']	[-1.72285721e-01 -1.24550559e-01 -1.94507703e-01 -5.33897698e-01 -6.05280280e-01 -4.05153364e-01 3.97360474e-01 3.38959664e-01 -1.10063739e-01 3.11732870e-02 2.64324069e-01 3.90674293e-01 -4.92813736e-01 -5.68573654e-01 -4.20033395e-01 -1.01537478e+00 7.27927089e-02 6.69475436e-01 -2.09834114e-01 1.30633965e-01 6.85794055e-02 6.60851598e-02 -1.70012951e+00 4.68946785e-01 8.70949805e-01 7.57261693e-01 4.70563799e-01 -3.73290363e-03 -3.20177704e-01 3.59605610e-01 -5.63232541e-01 2.17551425e-01 3.45959157e-01 -3.81219566e-01 -5.84867477e-01 5.59361935e-01 2.31287926e-01 -2.46074367e-02 -3.22536826e-01 1.07292783e+00 3.27885896e-01 6.83976188e-02 4.98168826e-01 -1.44433606e+00 -7.01844871e-01 7.39891171e-01 -6.74088061e-01 -3.20335358e-01 -1.93747967e-01 -1.24856420e-01 1.06396139e+00 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3883685b-e282-41e3-8c46-d48ad74bc2c3	xcoref-cross-document-coreference-resolution	2109.05252	null	https://arxiv.org/abs/2109.05252v1	https://arxiv.org/pdf/2109.05252v1.pdf	XCoref: Cross-document Coreference Resolution in the Wild	Datasets and methods for cross-document coreference resolution (CDCR) focus on events or entities with strict coreference relations. They lack, however, annotating and resolving coreference mentions with more abstract or loose relations that may occur when news articles report about controversial and polarized events. Bridging and loose coreference relations trigger associations that may lead to exposing news readers to bias by word choice and labeling. For example, coreferential mentions of "direct talks between U.S. President Donald Trump and Kim" such as "an extraordinary meeting following months of heated rhetoric" or "great chance to solve a world problem" form a more positive perception of this event. A step towards bringing awareness of bias by word choice and labeling is the reliable resolution of coreferences with high lexical diversity. We propose an unsupervised method named XCoref, which is a CDCR method that capably resolves not only previously prevalent entities, such as persons, e.g., "Donald Trump," but also abstractly defined concepts, such as groups of persons, "caravan of immigrants," events and actions, e.g., "marching to the U.S. border." In an extensive evaluation, we compare the proposed XCoref to a state-of-the-art CDCR method and a previous method TCA that resolves such complex coreference relations and find that XCoref outperforms these methods. Outperforming an established CDCR model shows that the new CDCR models need to be evaluated on semantically complex mentions with more loose coreference relations to indicate their applicability of models to resolve mentions in the "wild" of political news articles.	['Bela Gipp', 'Karsten Donnay', 'Felix Hamborg', 'Anastasia Zhukova']	2021-09-11	xcoref-cross-document-coreference-resolution-1	https://dl.acm.org/doi/abs/10.1007/978-3-030-96957-8_25	https://www.gipp.com/wp-content/papercite-data/pdf/zhukova2022.pdf	information-for-a-better-world-shaping-the	['cross-document-coreference-resolution']	['natural-language-processing']	[-3.79702821e-02 4.40151751e-01 -5.88316858e-01 -3.43800485e-01 -8.78289938e-01 -8.83652508e-01 1.14058125e+00 6.24959171e-01 -5.47272146e-01 1.17983365e+00 1.05526686e+00 -3.65230381e-01 -3.84074122e-01 -8.11857104e-01 -5.77351570e-01 -3.37294787e-01 1.39222905e-01 1.01570725e+00 2.77659118e-01 -9.49915469e-01 3.13533515e-01 3.47211570e-01 -1.32943451e+00 7.58217573e-01 5.86757481e-01 1.39519557e-01 2.56885830e-02 -1.50226668e-01 -3.77571940e-01 7.31060088e-01 -7.26939917e-01 -7.27110326e-01 -2.63042539e-01 -5.69080226e-02 -1.51462102e+00 -6.45439386e-01 4.79693264e-01 6.17166877e-01 3.71232480e-02 1.28574991e+00 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c9471da5-eb17-4751-91a5-123dd258e6dd	semi-supervised-learning-with-sparse	1610.00520	null	http://arxiv.org/abs/1610.00520v1	http://arxiv.org/pdf/1610.00520v1.pdf	Semi-supervised Learning with Sparse Autoencoders in Phone Classification	We propose the application of a semi-supervised learning method to improve the performance of acoustic modelling for automatic speech recognition based on deep neural net- works. As opposed to unsupervised initialisation followed by supervised fine tuning, our method takes advantage of both unlabelled and labelled data simultaneously through mini- batch stochastic gradient descent. We tested the method with varying proportions of labelled vs unlabelled observations in frame-based phoneme classification on the TIMIT database. Our experiments show that the method outperforms standard supervised training for an equal amount of labelled data and provides competitive error rates compared to state-of-the-art graph-based semi-supervised learning techniques.	['Giampiero Salvi', 'Akash Kumar Dhaka']	2016-10-03	null	null	null	null	['acoustic-modelling']	['speech']	[ 4.17425454e-01 5.02394676e-01 1.27654141e-02 -8.95415783e-01 -1.12231731e+00 -1.62463844e-01 8.06600809e-01 -1.01656877e-01 -7.42311358e-01 6.32900655e-01 1.55251175e-01 -7.02943325e-01 1.30674660e-01 -4.10581797e-01 -6.17791593e-01 -6.52601421e-01 -1.44044578e-01 9.61405516e-01 4.17896837e-01 -4.49331524e-03 -5.04740812e-02 2.94006914e-01 -1.36517549e+00 2.98395872e-01 3.01081598e-01 8.33041728e-01 2.54879534e-01 1.11542237e+00 -2.68568397e-01 1.00579250e+00 -5.18249273e-01 -2.93825507e-01 -1.97997287e-01 -4.76743788e-01 -8.68171453e-01 5.42911947e-01 5.65283656e-01 2.26174623e-01 -3.59446645e-01 8.28229368e-01 8.61485183e-01 5.24460196e-01 6.60844982e-01 -7.61781633e-01 -1.01324297e-01 9.12828505e-01 8.22796207e-03 4.51923281e-01 8.93520936e-02 -1.91926286e-01 8.80172729e-01 -1.13337851e+00 3.26393306e-01 1.45976985e+00 7.06309915e-01 5.01630306e-01 -1.26707554e+00 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-4.03160334e-01 -4.05933708e-02 4.36731547e-01 -4.52381253e-01 -8.69557679e-01 3.41607243e-01]	[14.424020767211914, 6.653627872467041]
f649718b-e642-4021-9d31-01590d8093df	deeply-supervised-rotation-equivariant	1807.02804	null	http://arxiv.org/abs/1807.02804v1	http://arxiv.org/pdf/1807.02804v1.pdf	Deeply Supervised Rotation Equivariant Network for Lesion Segmentation in Dermoscopy Images	Automatic lesion segmentation in dermoscopy images is an essential step for computer-aided diagnosis of melanoma. The dermoscopy images exhibits rotational and reflectional symmetry, however, this geometric property has not been encoded in the state-of-the-art convolutional neural networks based skin lesion segmentation methods. In this paper, we present a deeply supervised rotation equivariant network for skin lesion segmentation by extending the recent group rotation equivariant network~\cite{cohen2016group}. Specifically, we propose the G-upsampling and G-projection operations to adapt the rotation equivariant classification network for our skin lesion segmentation problem. To further increase the performance, we integrate the deep supervision scheme into our proposed rotation equivariant segmentation architecture. The whole framework is equivariant to input transformations, including rotation and reflection, which improves the network efficiency and thus contributes to the segmentation performance. We extensively evaluate our method on the ISIC 2017 skin lesion challenge dataset. The experimental results show that our rotation equivariant networks consistently excel the regular counterparts with the same model complexity under different experimental settings. Our best model achieves 77.23\%(JA) on the test dataset, outperforming the state-of-the-art challenging methods and further demonstrating the effectiveness of our proposed deeply supervised rotation equivariant segmentation network. Our best model also outperforms the state-of-the-art challenging methods, which further demonstrate the effectiveness of our proposed deeply supervised rotation equivariant segmentation network.	['Pheng-Ann Heng', 'Chi-Wing Fu', 'Lequan Yu', 'Xiaomeng Li']	2018-07-08	null	null	null	null	['skin-lesion-segmentation']	['medical']	[ 5.75584352e-01 2.35469580e-01 -4.09666449e-01 -2.15634584e-01 -5.93713164e-01 -3.23274821e-01 3.06200117e-01 -4.67076272e-01 -5.62887549e-01 2.02656299e-01 -1.69181395e-02 -4.95821089e-01 -1.21281736e-01 -5.30078769e-01 -5.61902165e-01 -9.54561532e-01 3.90682608e-01 6.92439377e-02 1.44963652e-01 -2.95955300e-01 -8.04333091e-02 7.80154228e-01 -8.03534329e-01 1.23853132e-01 1.11870503e+00 7.24773586e-01 -1.18708603e-01 8.54151428e-01 3.21052462e-01 3.48428428e-01 1.80371059e-03 -5.63603103e-01 2.76280642e-01 -4.88154441e-01 -1.12108231e+00 2.30171099e-01 6.85780406e-01 -3.92281532e-01 -3.58745158e-01 1.17435348e+00 4.87693250e-01 -2.99576491e-01 6.27355158e-01 -4.95644987e-01 -4.60352033e-01 5.12834549e-01 -8.76691878e-01 -9.51281413e-02 -1.53096214e-01 -1.28265092e-04 6.30556285e-01 -4.78453070e-01 1.03894985e+00 9.23816562e-01 9.65185523e-01 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6adaec8a-438a-4698-a1a9-dd993dcbc6a2	transcut-transparent-object-segmentation-from	1511.06853	null	http://arxiv.org/abs/1511.06853v1	http://arxiv.org/pdf/1511.06853v1.pdf	TransCut: Transparent Object Segmentation from a Light-Field Image	The segmentation of transparent objects can be very useful in computer vision applications. However, because they borrow texture from their background and have a similar appearance to their surroundings, transparent objects are not handled well by regular image segmentation methods. We propose a method that overcomes these problems using the consistency and distortion properties of a light-field image. Graph-cut optimization is applied for the pixel labeling problem. The light-field linearity is used to estimate the likelihood of a pixel belonging to the transparent object or Lambertian background, and the occlusion detector is used to find the occlusion boundary. We acquire a light field dataset for the transparent object, and use this dataset to evaluate our method. The results demonstrate that the proposed method successfully segments transparent objects from the background.	['Rin-ichiro Taniguchi', 'Yichao Xu', 'Atsushi Shimada', 'Hajime Nagahara']	2015-11-21	transcut-transparent-object-segmentation-from-1	http://openaccess.thecvf.com/content_iccv_2015/html/Xu_TransCut_Transparent_Object_ICCV_2015_paper.html	http://openaccess.thecvf.com/content_iccv_2015/papers/Xu_TransCut_Transparent_Object_ICCV_2015_paper.pdf	iccv-2015-12	['transparent-objects']	['computer-vision']	[ 3.68137211e-01 -1.40436456e-01 -5.07187471e-02 -4.65979815e-01 -9.95554477e-02 -3.90937299e-01 1.73125193e-01 -1.53844133e-01 -2.83407927e-01 5.76755345e-01 -5.41257381e-01 -2.64157020e-02 3.87161195e-01 -9.52373981e-01 -3.76118511e-01 -9.60148275e-01 5.49866855e-01 4.67361301e-01 1.08892846e+00 3.21683317e-01 2.35062882e-01 5.51200092e-01 -1.25819194e+00 1.18195908e-02 9.61807370e-01 9.28926587e-01 5.10915279e-01 2.62301087e-01 -6.26163900e-01 5.27506351e-01 -3.94434065e-01 -3.03375244e-01 6.59349263e-01 -3.38452607e-01 -6.65626049e-01 6.58356428e-01 6.69308841e-01 -5.06837130e-01 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d4113f70-3a58-4d39-bba1-347c965fbf0c	quantitative-evaluation-of-base-and-detail	1808.09411	null	http://arxiv.org/abs/1808.09411v1	http://arxiv.org/pdf/1808.09411v1.pdf	Quantitative Evaluation of Base and Detail Decomposition Filters Based on their Artifacts	This paper introduces a quantitative evaluation of filters that seek to separate an image into its large-scale variations, the base layer, and its fine-scale variations, the detail layer. Such methods have proliferated with the development of HDR imaging and the proposition of many new tone-mapping operators. We argue that an objective quality measurement for all methods can be based on their artifacts. To this aim, the four main recurrent artifacts are described and mathematically characterized. Among them two are classic, the luminance halo and the staircase effect, but we show the relevance of two more, the contrast halo and the compartmentalization effect. For each of these artifacts we design a test-pattern and its attached measurement formula. Then we fuse these measurements into a single quality mark, and obtain in that way a ranking method valid for all filters performing a base+detail decomposition. This synthetic ranking is applied to seven filters representative of the literature and shown to agree with expert artifact rejection criteria.	['Jean-Michel Morel', 'Charles Hessel']	2018-08-28	null	null	null	null	['tone-mapping']	['computer-vision']	[ 3.57502818e-01 -1.32599294e-01 4.48824346e-01 -1.85705647e-01 -6.67861342e-01 -3.86291355e-01 6.97874427e-01 1.03889115e-01 -1.47786230e-01 8.52655232e-01 2.33997494e-01 4.32798304e-02 -4.21041578e-01 -5.37120223e-01 -1.92682579e-01 -8.47444355e-01 -2.24477388e-02 -3.68502289e-02 6.25639379e-01 -3.16566169e-01 3.41376245e-01 6.57156825e-01 -1.87401521e+00 3.21049780e-01 1.01746261e+00 1.11295438e+00 7.10977465e-02 5.52982986e-01 -8.91937390e-02 7.58853674e-01 -9.29921567e-01 -3.62945706e-01 2.22928658e-01 -7.15909600e-01 -6.23518586e-01 3.46249372e-01 3.51402640e-01 -1.41950712e-01 -7.98937771e-03 1.14233351e+00 5.73993266e-01 -2.38603160e-01 6.21379316e-01 -7.41863370e-01 -5.20395041e-01 2.49091700e-01 -4.52403545e-01 2.38092348e-01 5.28728187e-01 9.18223932e-02 5.50598979e-01 -9.52643931e-01 6.75596774e-01 9.35190141e-01 6.44912124e-01 2.06661627e-01 -1.37295544e+00 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6.51106715e-01 5.90422630e-01 -5.96441567e-01 -7.63998032e-01 9.10676792e-02]	[11.293927192687988, -2.236971378326416]
6237e181-ba14-42eb-9021-3fc8d3ec5c8f	enriching-entity-grids-and-graphs-with	null	null	https://aclanthology.org/W15-5619	https://aclanthology.org/W15-5619.pdf	Enriching entity grids and graphs with discourse relations: the impact in local coherence evaluation	null	["M{\\'a}rcio de S. Dias", 'Thiago A. S. Pardo']	2015-11-01	null	null	null	ws-2015-11	['coherence-evaluation']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 9.22211111e-01 5.37373662e-01 4.49454933e-02 1.08379531e+00 -1.42548037e+00 -5.91619551e-01 6.10313773e-01 -4.01565880e-02 1.21627934e-01 1.03678203e+00 -5.39447069e-01 1.19919395e+00 -1.13026452e+00 7.20913768e-01 1.26888943e+00 8.66221786e-01 5.44149756e-01 -1.22286928e+00 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-7.84516707e-02 2.89751232e-01 -8.00943017e-01 -7.83523321e-01 2.83909619e-01]	[-7.379876136779785, 3.7160580158233643]
65c9f6a2-f128-48d2-8cec-010ee40d0cb2	regclr-a-self-supervised-framework-for	2211.01165	null	https://arxiv.org/abs/2211.01165v1	https://arxiv.org/pdf/2211.01165v1.pdf	RegCLR: A Self-Supervised Framework for Tabular Representation Learning in the Wild	Recent advances in self-supervised learning (SSL) using large models to learn visual representations from natural images are rapidly closing the gap between the results produced by fully supervised learning and those produced by SSL on downstream vision tasks. Inspired by this advancement and primarily motivated by the emergence of tabular and structured document image applications, we investigate which self-supervised pretraining objectives, architectures, and fine-tuning strategies are most effective. To address these questions, we introduce RegCLR, a new self-supervised framework that combines contrastive and regularized methods and is compatible with the standard Vision Transformer architecture. Then, RegCLR is instantiated by integrating masked autoencoders as a representative example of a contrastive method and enhanced Barlow Twins as a representative example of a regularized method with configurable input image augmentations in both branches. Several real-world table recognition scenarios (e.g., extracting tables from document images), ranging from standard Word and Latex documents to even more challenging electronic health records (EHR) computer screen images, have been shown to benefit greatly from the representations learned from this new framework, with detection average-precision (AP) improving relatively by 4.8% for Table, 11.8% for Column, and 11.1% for GUI objects over a previous fully supervised baseline on real-world EHR screen images.	['Varun Ganapathi', 'Byung-Hak Kim', 'Weiyao Wang']	2022-11-02	null	null	null	null	['table-recognition']	['computer-vision']	[ 6.31117404e-01 2.52129078e-01 -5.99879734e-02 -3.57405365e-01 -8.12329769e-01 -6.41113400e-01 7.76159704e-01 3.17268103e-01 -3.12974691e-01 5.14360070e-01 3.52633715e-01 -3.18092048e-01 -4.63165231e-02 -6.13295019e-01 -1.04062927e+00 -4.68703091e-01 1.09478109e-01 5.79546690e-01 -1.77033022e-01 -1.87719181e-01 2.53608376e-02 4.10165101e-01 -1.49011314e+00 9.69169855e-01 6.19427979e-01 1.06715369e+00 1.85306624e-01 8.03751230e-01 -3.29779476e-01 1.24990213e+00 -6.60983682e-01 -3.90110135e-01 2.01633886e-01 -1.54665008e-01 -8.07578266e-01 4.02975440e-01 6.49074256e-01 -2.56220967e-01 -3.09900969e-01 8.56400728e-01 4.67236876e-01 -2.16735095e-01 5.79723775e-01 -1.00968790e+00 -1.24563718e+00 8.53754759e-01 -5.26126385e-01 1.73568919e-01 2.89471418e-01 3.60939950e-01 8.91214848e-01 -9.92895901e-01 6.36958182e-01 1.29347241e+00 6.69505715e-01 5.39214611e-01 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-2.71924645e-01 4.86657679e-01 5.32045484e-01 -4.25508678e-01 -1.58118129e-01 3.74639660e-01]	[10.00249195098877, 2.226872444152832]
fe3023dd-7b2d-424e-b893-148592e91859	plug-and-play-autoencoders-for-conditional	2010.02983	null	https://arxiv.org/abs/2010.02983v2	https://arxiv.org/pdf/2010.02983v2.pdf	Plug and Play Autoencoders for Conditional Text Generation	Text autoencoders are commonly used for conditional generation tasks such as style transfer. We propose methods which are plug and play, where any pretrained autoencoder can be used, and only require learning a mapping within the autoencoder's embedding space, training embedding-to-embedding (Emb2Emb). This reduces the need for labeled training data for the task and makes the training procedure more efficient. Crucial to the success of this method is a loss term for keeping the mapped embedding on the manifold of the autoencoder and a mapping which is trained to navigate the manifold by learning offset vectors. Evaluations on style transfer tasks both with and without sequence-to-sequence supervision show that our method performs better than or comparable to strong baselines while being up to four times faster.	['James Henderson', 'Noah A. Smith', 'Ivan Montero', 'Nikolaos Pappas', 'Florian Mai']	2020-10-06	null	https://aclanthology.org/2020.emnlp-main.491	https://aclanthology.org/2020.emnlp-main.491.pdf	emnlp-2020-11	['conditional-text-generation']	['natural-language-processing']	[ 1.19162448e-01 4.05372769e-01 -3.92430983e-02 -4.19067949e-01 -4.22392249e-01 -5.78932524e-01 8.53439450e-01 -3.02304059e-01 -5.47555089e-01 8.23127687e-01 4.64227706e-01 -4.93810892e-01 5.75013697e-01 -7.82766998e-01 -1.03278267e+00 -5.01916468e-01 3.40358227e-01 6.41610146e-01 7.79407695e-02 -4.00624007e-01 -1.63660526e-01 2.29163364e-01 -1.01265585e+00 1.84729934e-01 6.28238082e-01 6.09773338e-01 2.82938927e-01 8.11686099e-01 -2.69790828e-01 7.89396763e-01 -6.49808586e-01 -7.12894917e-01 1.93538621e-01 -7.33089387e-01 -1.10737348e+00 2.90353913e-02 2.96691388e-01 -6.59234762e-01 -4.55493718e-01 6.44274712e-01 2.65864581e-01 3.58978689e-01 9.15472090e-01 -1.04246914e+00 -9.26110566e-01 4.28879648e-01 -1.23267204e-01 -8.69766474e-02 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50044378-2fdb-4c2d-bcbf-864809268ab0	wonderm-skin-lesion-classification-with-fine	1808.03426	null	https://arxiv.org/abs/1808.03426v3	https://arxiv.org/pdf/1808.03426v3.pdf	WonDerM: Skin Lesion Classification with Fine-tuned Neural Networks	As skin cancer is one of the most frequent cancers globally, accurate, non-invasive dermoscopy-based diagnosis becomes essential and promising. A task of the Part 3 of the ISIC Skin Image Analysis Challenge at MICCAI 2018 is to predict seven disease classes with skin lesion images, including melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis / Bowen's disease (intraepithelial carcinoma) (AKIEC), benign keratosis (solar lentigo / seborrheic keratosis / lichen planus-like keratosis) (BKL), dermatofibroma (DF) and vascular lesion (VASC) as defined by the International Dermatology Society. In this work, we design the WonDerM pipeline, that resamples the preprocessed skin lesion images, builds neural network architecture fine-tuned with segmentation task data (the Part 1), and uses an ensemble method to classify the seven skin diseases. Our model achieved an accuracy of 0.899 and 0.785 in the validation set and test set, respectively.	['Hong-Hee Won', 'Sang-Hyuk Jung', 'Yeong Chan Lee']	2018-08-10	null	null	null	null	['skin-lesion-classification']	['medical']	[ 8.07025552e-01 1.42520741e-01 -2.93841392e-01 -5.61200175e-03 -4.07197833e-01 -5.11182010e-01 6.56449437e-01 5.37493117e-02 -3.62144172e-01 6.77740872e-01 -2.30390742e-01 -5.80826879e-01 -1.90944821e-01 -5.96515119e-01 -2.35115394e-01 -9.23537076e-01 2.50659823e-01 9.80952755e-02 2.59735852e-01 6.46934137e-02 7.82293007e-02 4.75152612e-01 -1.14463842e+00 5.43562710e-01 1.37509704e+00 1.12966704e+00 -3.03563654e-01 1.30672550e+00 -1.00757312e-02 6.72222316e-01 -2.07457319e-01 -7.26108253e-01 -8.49815086e-02 -3.33482772e-01 -9.62378204e-01 8.19401518e-02 8.36749673e-01 -1.23855084e-01 4.22878973e-02 1.12450898e+00 2.60821700e-01 -5.61797261e-01 1.23007023e+00 -9.12588179e-01 -3.10473800e-01 -1.62489876e-01 -1.06320357e+00 -1.81986675e-01 1.06906965e-01 1.86993822e-01 4.33886573e-02 -3.04615706e-01 1.13541579e+00 7.26337850e-01 9.65018570e-01 1.08082509e+00 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b7686392-1f20-4ae0-8ade-dcdcf34b9597	maximal-divergence-sequential-autoencoder-for	null	null	https://openreview.net/forum?id=ByloIiCqYQ	https://openreview.net/pdf?id=ByloIiCqYQ	Maximal Divergence Sequential Autoencoder for Binary Software Vulnerability Detection	Due to the sharp increase in the severity of the threat imposed by software vulnerabilities, the detection of vulnerabilities in binary code has become an important concern in the software industry, such as the embedded systems industry, and in the field of computer security. However, most of the work in binary code vulnerability detection has relied on handcrafted features which are manually chosen by a select few, knowledgeable domain experts. In this paper, we attempt to alleviate this severe binary vulnerability detection bottleneck by leveraging recent advances in deep learning representations and propose the Maximal Divergence Sequential Auto-Encoder. In particular, latent codes representing vulnerable and non-vulnerable binaries are encouraged to be maximally divergent, while still being able to maintain crucial information from the original binaries. We conducted extensive experiments to compare and contrast our proposed methods with the baselines, and the results show that our proposed methods outperform the baselines in all performance measures of interest.	['Trung Le', 'Dinh Phung', 'Tue Le', 'Tuan Nguyen', 'Olivier De Vel', 'Paul Montague', 'Lizhen Qu']	2019-05-01	null	null	null	iclr-2019-5	['vulnerability-detection', 'computer-security']	['miscellaneous', 'miscellaneous']	[ 9.76498351e-02 -1.70609877e-01 -2.92847455e-01 -2.50800371e-01 -4.92636651e-01 -9.92974162e-01 3.30755055e-01 4.44291651e-01 -1.64723977e-01 4.86303359e-01 1.23284280e-01 -6.79544449e-01 -2.36363355e-02 -6.84638619e-01 -5.16550004e-01 -5.60711503e-01 -2.92635590e-01 -5.36254272e-02 4.40147430e-01 -2.62842327e-01 5.83287537e-01 2.61922330e-01 -1.10846043e+00 9.41252932e-02 8.37275565e-01 9.74848390e-01 -1.63953498e-01 5.29466629e-01 1.44118562e-01 7.11006105e-01 -5.20020604e-01 -8.75955224e-01 3.81052017e-01 -1.99023724e-01 -5.52638829e-01 -2.56612271e-01 1.15310296e-01 -3.53364199e-01 -6.82787359e-01 1.71878648e+00 3.52091551e-01 -2.33935133e-01 4.21299607e-01 -1.10536027e+00 -5.72911918e-01 9.09768283e-01 -6.67807221e-01 5.34138978e-01 4.75015529e-02 1.41029418e-01 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b44b944e-e97b-4b46-a4f7-32f54bee89d9	explore-and-match-end-to-end-video-grounding	2201.10168	null	https://arxiv.org/abs/2201.10168v4	https://arxiv.org/pdf/2201.10168v4.pdf	Explore-And-Match: Bridging Proposal-Based and Proposal-Free With Transformer for Sentence Grounding in Videos	Natural Language Video Grounding (NLVG) aims to localize time segments in an untrimmed video according to sentence queries. In this work, we present a new paradigm named Explore-And-Match for NLVG that seamlessly unifies the strengths of two streams of NLVG methods: proposal-free and proposal-based; the former explores the search space to find time segments directly, and the latter matches the predefined time segments with ground truths. To achieve this, we formulate NLVG as a set prediction problem and design an end-to-end trainable Language Video Transformer (LVTR) that can enjoy two favorable properties, which are rich contextualization power and parallel decoding. We train LVTR with two losses. First, temporal localization loss allows time segments of all queries to regress targets (explore). Second, set guidance loss couples every query with their respective target (match). To our surprise, we found that training schedule shows divide-and-conquer-like pattern: time segments are first diversified regardless of the target, then coupled with each target, and fine-tuned to the target again. Moreover, LVTR is highly efficient and effective: it infers faster than previous baselines (by 2X or more) and sets competitive results on two NLVG benchmarks (ActivityCaptions and Charades-STA). Codes are available at https://github.com/sangminwoo/Explore-And-Match.	['Changick Kim', 'Minki Jeong', 'Sumin Lee', 'Inyong Koo', 'Jinyoung Park', 'Sangmin Woo']	2022-01-25	null	null	null	null	['video-grounding']	['computer-vision']	[ 4.97961678e-02 -2.60288604e-02 -6.87691391e-01 -4.18217599e-01 -1.23830807e+00 -7.05852509e-01 5.29866219e-01 -1.19167186e-01 -4.46012974e-01 6.54868841e-01 3.23422819e-01 -1.93087429e-01 -1.55974776e-02 -6.07262671e-01 -1.02978873e+00 -6.21194899e-01 -1.04515053e-01 3.71583968e-01 6.32523715e-01 -1.32946953e-01 3.89040401e-03 1.71762615e-01 -1.35804081e+00 5.45442522e-01 5.68294704e-01 1.09751177e+00 4.12315339e-01 5.97280622e-01 2.61893243e-01 1.03879464e+00 -1.75996587e-01 -4.55052674e-01 1.86352462e-01 -5.45880556e-01 -6.36482239e-01 -1.42847002e-02 5.20648062e-01 -2.04295635e-01 -8.68528247e-01 6.78587019e-01 4.28114414e-01 2.45784536e-01 2.18955815e-01 -1.43103850e+00 -4.70070571e-01 6.25596285e-01 -5.31744838e-01 4.09984261e-01 3.87998521e-01 3.87963295e-01 1.15270078e+00 -7.20141768e-01 8.89159739e-01 1.07163286e+00 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8e463456-a4a5-46bc-bbc0-c28427b68d7b	non-contact-atrial-fibrillation-detection	2110.07610	null	https://arxiv.org/abs/2110.07610v2	https://arxiv.org/pdf/2110.07610v2.pdf	Non-contact Atrial Fibrillation Detection from Face Videos by Learning Systolic Peaks	Objective: We propose a non-contact approach for atrial fibrillation (AF) detection from face videos. Methods: Face videos, electrocardiography (ECG), and contact photoplethysmography (PPG) from 100 healthy subjects and 100 AF patients are recorded. Data recordings from healthy subjects are all labeled as healthy. Two cardiologists evaluated ECG recordings of patients and labeled each recording as AF, sinus rhythm (SR), or atrial flutter (AFL). We use the 3D convolutional neural network for remote PPG monitoring and propose a novel loss function (Wasserstein distance) to use the timing of systolic peaks from contact PPG as the label for our model training. Then a set of heart rate variability (HRV) features are calculated from the inter-beat intervals, and a support vector machine (SVM) classifier is trained with HRV features. Results: Our proposed method can accurately extract systolic peaks from face videos for AF detection. The proposed method is trained with subject-independent 10-fold cross-validation with 30s video clips and tested on two tasks. 1) Classification of healthy versus AF: the accuracy, sensitivity, and specificity are 96.00%, 95.36%, and 96.12%. 2) Classification of SR versus AF: the accuracy, sensitivity, and specificity are 95.23%, 98.53%, and 91.12%. In addition, we also demonstrate the feasibility of non-contact AFL detection. Conclusion: We achieve good performance of non-contact AF detection by learning systolic peaks. Significance: non-contact AF detection can be used for self-screening of AF symptoms for suspectable populations at home or self-monitoring of AF recurrence after treatment for chronic patients.	['Xiaobai Li', 'Tapio Seppänen', 'Mikko Tulppo', 'Juhani Junttila', 'Zhaodong Sun']	2021-10-14	null	null	null	null	['photoplethysmography-ppg', 'heart-rate-variability', 'atrial-fibrillation-detection', 'electrocardiography-ecg']	['medical', 'medical', 'medical', 'methodology']	[ 3.59710813e-01 -1.88592479e-01 -1.93929762e-01 -4.71704990e-01 -7.09845960e-01 -6.85457289e-01 -2.43057594e-01 -2.96631038e-01 -5.45117147e-02 7.40146995e-01 -7.32071400e-02 -2.96614438e-01 2.07141042e-04 -5.66252053e-01 -1.20262951e-01 -8.20420504e-01 -6.38061583e-01 2.01114953e-01 -5.58852375e-01 4.11420941e-01 -8.55894610e-02 7.63903797e-01 -1.11883974e+00 2.71587163e-01 7.60109842e-01 1.57934225e+00 -7.05458999e-01 8.42087150e-01 3.89549255e-01 1.97317228e-01 -8.53501916e-01 1.40095383e-01 2.94783235e-01 -8.26973677e-01 -2.30117545e-01 -7.87264332e-02 6.39455795e-01 -3.63246232e-01 -2.29766697e-01 6.38645411e-01 1.17013800e+00 -4.17012900e-01 6.50741160e-01 -1.12446892e+00 -4.28162277e-01 1.57109827e-01 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bad44840-2b8d-4cbf-b509-7e862a996cd3	svarah-evaluating-english-asr-systems-on	2305.15760	null	https://arxiv.org/abs/2305.15760v1	https://arxiv.org/pdf/2305.15760v1.pdf	Svarah: Evaluating English ASR Systems on Indian Accents	India is the second largest English-speaking country in the world with a speaker base of roughly 130 million. Thus, it is imperative that automatic speech recognition (ASR) systems for English should be evaluated on Indian accents. Unfortunately, Indian speakers find a very poor representation in existing English ASR benchmarks such as LibriSpeech, Switchboard, Speech Accent Archive, etc. In this work, we address this gap by creating Svarah, a benchmark that contains 9.6 hours of transcribed English audio from 117 speakers across 65 geographic locations throughout India, resulting in a diverse range of accents. Svarah comprises both read speech and spontaneous conversational data, covering various domains, such as history, culture, tourism, etc., ensuring a diverse vocabulary. We evaluate 6 open source ASR models and 2 commercial ASR systems on Svarah and show that there is clear scope for improvement on Indian accents. Svarah as well as all our code will be publicly available.	['Mitesh M. Khapra', 'Pratyush Kumar', 'Kaushal Bhogale', 'Abhigyan Raman', 'Janki Nawale', 'Sai Sundaresan', 'Vignesh Nagarajan', 'Sakshi Joshi', 'Tahir Javed']	2023-05-25	null	null	null	null	['automatic-speech-recognition']	['speech']	[-2.72964001e-01 -4.13382659e-03 6.14962727e-02 -6.24466300e-01 -1.41610146e+00 -8.64886999e-01 5.40087044e-01 -1.51647985e-01 -5.39270818e-01 8.21308732e-01 9.28862572e-01 -5.48914015e-01 3.19080770e-01 -3.94462347e-01 -1.57386258e-01 -4.34327722e-01 1.30452499e-01 6.97697103e-01 -1.92501873e-01 -8.59633267e-01 1.27498358e-01 3.85723591e-01 -9.62080240e-01 -9.07795057e-02 6.61665380e-01 5.39041817e-01 1.34398580e-01 8.96645546e-01 -1.83842212e-01 9.27065194e-01 -1.15110278e+00 -5.17661393e-01 1.78953144e-03 -2.62464553e-01 -1.21806943e+00 -1.14384152e-01 4.29824442e-01 -5.62020652e-02 -5.23249269e-01 8.19244325e-01 1.03766537e+00 2.93738514e-01 9.93910506e-02 -8.25026274e-01 -8.80893648e-01 1.26689601e+00 -1.33699611e-01 5.53235352e-01 6.07157290e-01 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6a99e919-e59a-4009-abc4-01ae3074bffd	deep-tensor-networks-with-matrix-product	2209.09098	null	https://arxiv.org/abs/2209.09098v1	https://arxiv.org/pdf/2209.09098v1.pdf	Deep tensor networks with matrix product operators	We introduce deep tensor networks, which are exponentially wide neural networks based on the tensor network representation of the weight matrices. We evaluate the proposed method on the image classification (MNIST, FashionMNIST) and sequence prediction (cellular automata) tasks. In the image classification case, deep tensor networks improve our matrix product state baselines and achieve 0.49% error rate on MNIST and 8.3% error rate on FashionMNIST. In the sequence prediction case, we demonstrate an exponential improvement in the number of parameters compared to the one-layer tensor network methods. In both cases, we discuss the non-uniform and the uniform tensor network models and show that the latter generalizes well to different input sizes.	['Bojan Žunkovič']	2022-09-16	null	null	null	null	['tensor-networks']	['methodology']	[ 1.40687257e-01 -3.73140931e-01 -2.41938382e-01 1.18532576e-01 -1.13328449e-01 -8.59695017e-01 4.93567854e-01 4.93891276e-02 -5.56876898e-01 5.09272099e-01 1.82738543e-01 -7.14761376e-01 -5.81652485e-02 -5.00905335e-01 -9.59500849e-01 -8.20292115e-01 -3.63948941e-01 5.22261798e-01 2.61998177e-01 -3.18262517e-01 1.66884914e-01 4.56196129e-01 -9.98252690e-01 6.03035808e-01 2.09184140e-01 1.50633180e+00 -3.25212210e-01 1.29353976e+00 1.25781327e-01 1.22647798e+00 -7.57610053e-02 -8.58656287e-01 4.47215259e-01 3.14374477e-01 -1.16343212e+00 1.31371379e-01 5.16867578e-01 -3.66197407e-01 -9.30634081e-01 9.31385875e-01 1.62214398e-01 4.65039238e-02 7.57188261e-01 -1.21000028e+00 -1.11478424e+00 9.22105670e-01 -1.37744218e-01 3.59921873e-01 -4.32564437e-01 2.05266729e-01 1.29942870e+00 -8.35950017e-01 4.02820617e-01 1.21732092e+00 9.73157465e-01 1.95295587e-01 -1.37770760e+00 -2.60937572e-01 -1.41956389e-01 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6cc1aeb2-4bde-4911-83b4-dc8ae66986fc	cxtrack-improving-3d-point-cloud-tracking	2211.08542	null	https://arxiv.org/abs/2211.08542v2	https://arxiv.org/pdf/2211.08542v2.pdf	CXTrack: Improving 3D Point Cloud Tracking with Contextual Information	3D single object tracking plays an essential role in many applications, such as autonomous driving. It remains a challenging problem due to the large appearance variation and the sparsity of points caused by occlusion and limited sensor capabilities. Therefore, contextual information across two consecutive frames is crucial for effective object tracking. However, points containing such useful information are often overlooked and cropped out in existing methods, leading to insufficient use of important contextual knowledge. To address this issue, we propose CXTrack, a novel transformer-based network for 3D object tracking, which exploits ConteXtual information to improve the tracking results. Specifically, we design a target-centric transformer network that directly takes point features from two consecutive frames and the previous bounding box as input to explore contextual information and implicitly propagate target cues. To achieve accurate localization for objects of all sizes, we propose a transformer-based localization head with a novel center embedding module to distinguish the target from distractors. Extensive experiments on three large-scale datasets, KITTI, nuScenes and Waymo Open Dataset, show that CXTrack achieves state-of-the-art tracking performance while running at 34 FPS.	['Song-Hai Zhang', 'Yu-Kun Lai', 'Yuan-Chen Guo', 'Tian-Xing Xu']	2022-11-12	null	http://openaccess.thecvf.com//content/CVPR2023/html/Xu_CXTrack_Improving_3D_Point_Cloud_Tracking_With_Contextual_Information_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Xu_CXTrack_Improving_3D_Point_Cloud_Tracking_With_Contextual_Information_CVPR_2023_paper.pdf	cvpr-2023-1	['3d-single-object-tracking', '3d-object-tracking']	['computer-vision', 'computer-vision']	[-1.21552251e-01 -6.31083786e-01 -2.78519571e-01 -2.14382708e-01 -4.64388609e-01 -5.85674822e-01 3.56484741e-01 5.67711145e-02 -4.74670798e-01 4.94658619e-01 -4.26930282e-03 -1.43555207e-02 -5.00811525e-02 -4.98252928e-01 -8.44835520e-01 -7.85578012e-01 3.44891921e-02 6.78930059e-02 1.00443816e+00 -1.54215693e-01 -3.73825915e-02 6.62300229e-01 -1.65892911e+00 -1.61716729e-01 6.44065738e-01 1.27277040e+00 3.47403079e-01 1.32601619e-01 -2.41544783e-01 5.64564228e-01 -4.80254412e-01 -1.63026705e-01 5.23624837e-01 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6608b1b8-594d-4ec1-958b-77829269241f	leveraging-predictions-in-power-system	2305.12044	null	https://arxiv.org/abs/2305.12044v1	https://arxiv.org/pdf/2305.12044v1.pdf	Leveraging Predictions in Power System Frequency Control: an Adaptive Approach	Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations. In recent years, a number of advanced controllers have been designed to optimize frequency control. These controllers, however, almost always assume that the net load in the system remains constant over a sufficiently long time. Given the intermittent and uncertain nature of renewable resources, it is becoming important to explicitly consider net load that is time-varying. This paper proposes an adaptive approach to frequency control in power systems with significant time-varying net load. We leverage the advances in short-term load forecasting, where the net load in the system can be accurately predicted using weather and other features. We integrate these predictions into the design of adaptive controllers, which can be seamlessly combined with most existing controllers including conventional droop control and emerging neural network-based controllers. We prove that the overall control architecture achieves frequency restoration decentralizedly. Case studies verify that the proposed method improves both transient and frequency-restoration performances compared to existing approaches.	['Baosen Zhang', 'Yuanyuan Shi', 'Guanya Shi', 'Wenqi Cui']	2023-05-20	null	null	null	null	['load-forecasting']	['miscellaneous']	[-2.68889666e-01 -2.15511769e-01 -4.53139842e-01 2.54601717e-01 1.61577724e-02 -9.38457549e-01 2.99098581e-01 8.78948998e-03 5.44606984e-01 1.44268560e+00 -2.35621221e-02 -1.06605776e-01 -5.19538105e-01 -9.12986100e-01 -6.88864961e-02 -1.12200248e+00 -4.76062745e-01 1.53365418e-01 -2.85532683e-01 -6.86298966e-01 -8.77381302e-03 5.48334420e-01 -1.22192979e+00 -6.37867212e-01 1.24439168e+00 1.03139079e+00 7.05065578e-02 5.12578309e-01 6.80420876e-01 4.34322923e-01 -9.70255196e-01 7.32098401e-01 2.33525783e-01 -2.32937232e-01 -8.18960145e-02 -1.91078067e-01 -4.75627035e-01 -3.98148417e-01 -4.34645563e-01 1.22050667e+00 7.07619548e-01 3.49504173e-01 4.71202880e-01 -1.57615674e+00 -6.64974630e-01 5.70182979e-01 -4.40642744e-01 4.62747544e-01 6.29844591e-02 1.69734344e-01 7.87185371e-01 -3.77706856e-01 1.22106723e-01 6.93679512e-01 6.12469494e-01 2.27849528e-01 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1261815b-6d67-4ad5-9608-d426a5dca321	emotiongesture-audio-driven-diverse-emotional	2305.18891	null	https://arxiv.org/abs/2305.18891v1	https://arxiv.org/pdf/2305.18891v1.pdf	EmotionGesture: Audio-Driven Diverse Emotional Co-Speech 3D Gesture Generation	Generating vivid and diverse 3D co-speech gestures is crucial for various applications in animating virtual avatars. While most existing methods can generate gestures from audio directly, they usually overlook that emotion is one of the key factors of authentic co-speech gesture generation. In this work, we propose EmotionGesture, a novel framework for synthesizing vivid and diverse emotional co-speech 3D gestures from audio. Considering emotion is often entangled with the rhythmic beat in speech audio, we first develop an Emotion-Beat Mining module (EBM) to extract the emotion and audio beat features as well as model their correlation via a transcript-based visual-rhythm alignment. Then, we propose an initial pose based Spatial-Temporal Prompter (STP) to generate future gestures from the given initial poses. STP effectively models the spatial-temporal correlations between the initial poses and the future gestures, thus producing the spatial-temporal coherent pose prompt. Once we obtain pose prompts, emotion, and audio beat features, we will generate 3D co-speech gestures through a transformer architecture. However, considering the poses of existing datasets often contain jittering effects, this would lead to generating unstable gestures. To address this issue, we propose an effective objective function, dubbed Motion-Smooth Loss. Specifically, we model motion offset to compensate for jittering ground-truth by forcing gestures to be smooth. Last, we present an emotion-conditioned VAE to sample emotion features, enabling us to generate diverse emotional results. Extensive experiments demonstrate that our framework outperforms the state-of-the-art, achieving vivid and diverse emotional co-speech 3D gestures.	['Xin Yu', 'Haoran Xin', 'Jie Hou', 'Lincheng Li', 'Chen Liu', 'Xingqun Qi']	2023-05-30	null	null	null	null	['gesture-generation']	['robots']	[-4.24325392e-02 -1.92545190e-01 1.71615139e-01 -3.01461577e-01 -7.69022465e-01 -6.46212876e-01 6.87399209e-01 -7.24481523e-01 2.06228346e-01 1.62252828e-01 4.95945275e-01 1.65629640e-01 1.19129263e-01 -3.27619463e-01 -5.16575515e-01 -7.08553016e-01 -1.10487796e-01 1.49322689e-01 -1.14686787e-01 -3.77921671e-01 -9.39795095e-03 4.94749576e-01 -1.84108484e+00 2.76980340e-01 5.92198014e-01 9.77905452e-01 4.49949019e-02 9.33048427e-01 -3.22953671e-01 5.95115066e-01 -1.10567713e+00 -1.92016765e-01 1.23368450e-01 -8.37353170e-01 -4.15835083e-01 1.49649635e-01 5.32466620e-02 -3.77934068e-01 -2.59005338e-01 6.31335020e-01 8.47269893e-01 2.90361375e-01 7.85910189e-01 -1.57290804e+00 -1.09508887e-01 4.39328372e-01 -5.88262260e-01 -3.92772675e-01 8.07600796e-01 3.31799984e-01 9.43392456e-01 -9.65131044e-01 9.50202942e-01 1.56425452e+00 4.59241897e-01 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733eeaca-da0e-4433-b037-a8a795908afc	sgm-net-semantic-guided-matting-net	2208.07496	null	https://arxiv.org/abs/2208.07496v1	https://arxiv.org/pdf/2208.07496v1.pdf	SGM-Net: Semantic Guided Matting Net	Human matting refers to extracting human parts from natural images with high quality, including human detail information such as hair, glasses, hat, etc. This technology plays an essential role in image synthesis and visual effects in the film industry. When the green screen is not available, the existing human matting methods need the help of additional inputs (such as trimap, background image, etc.), or the model with high computational cost and complex network structure, which brings great difficulties to the application of human matting in practice. To alleviate such problems, most existing methods (such as MODNet) use multi-branches to pave the way for matting through segmentation, but these methods do not make full use of the image features and only utilize the prediction results of the network as guidance information. Therefore, we propose a module to generate foreground probability map and add it to MODNet to obtain Semantic Guided Matting Net (SGM-Net). Under the condition of only one image, we can realize the human matting task. We verify our method on the P3M-10k dataset. Compared with the benchmark, our method has significantly improved in various evaluation indicators.	['Chun Liu', 'Mengjie Hu', 'Donghan Yang', 'Wenfeng Sun', 'Qing Song']	2022-08-16	null	null	null	null	['image-matting']	['computer-vision']	[ 3.80990237e-01 -4.05038930e-02 5.34380600e-02 -1.98228776e-01 1.39056653e-01 -5.64619415e-02 2.08674967e-01 -5.06663322e-01 -2.54437864e-01 5.58948576e-01 -8.79353434e-02 -1.92667440e-01 2.21711427e-01 -1.08796883e+00 -7.86852181e-01 -7.42545903e-01 6.88521624e-01 3.25696468e-01 8.03530753e-01 -3.08589250e-01 3.21891047e-02 5.55267818e-02 -1.20246840e+00 4.26809341e-01 1.24424374e+00 7.99414396e-01 6.00417554e-01 2.90701151e-01 -6.80533707e-01 6.47255778e-01 -6.05079830e-01 -4.90379155e-01 3.55055541e-01 -6.72992110e-01 -4.66890663e-01 6.86404765e-01 7.96415806e-02 -5.97054720e-01 -4.92961794e-01 1.26496649e+00 2.18047291e-01 -1.12679087e-01 5.08181751e-01 -1.33124220e+00 -4.16285723e-01 6.93266749e-01 -8.76483679e-01 -6.14609150e-03 1.90870094e-04 3.91979396e-01 3.69358093e-01 -7.74926424e-01 4.67761964e-01 1.41769028e+00 3.57726872e-01 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aab2f32b-1626-473b-b273-ca468e2fec72	a-comparison-of-time-based-models-for	2306.13076	null	https://arxiv.org/abs/2306.13076v1	https://arxiv.org/pdf/2306.13076v1.pdf	A Comparison of Time-based Models for Multimodal Emotion Recognition	Emotion recognition has become an important research topic in the field of human-computer interaction. Studies on sound and videos to understand emotions focused mainly on analyzing facial expressions and classified 6 basic emotions. In this study, the performance of different sequence models in multi-modal emotion recognition was compared. The sound and images were first processed by multi-layered CNN models, and the outputs of these models were fed into various sequence models. The sequence model is GRU, Transformer, LSTM and Max Pooling. Accuracy, precision, and F1 Score values of all models were calculated. The multi-modal CREMA-D dataset was used in the experiments. As a result of the comparison of the CREMA-D dataset, GRU-based architecture with 0.640 showed the best result in F1 score, LSTM-based architecture with 0.699 in precision metric, while sensitivity showed the best results over time with Max Pooling-based architecture with 0.620. As a result, it has been observed that the sequence models compare performances close to each other.	['Sena Nur Cavsak', 'Selahattin Serdar Helli', 'Ege Kesim']	2023-06-22	null	null	null	null	['multimodal-emotion-recognition', 'multimodal-emotion-recognition']	['computer-vision', 'speech']	[-6.75557852e-02 -4.25435841e-01 3.43277037e-01 -3.49940717e-01 -2.58458823e-01 -2.79961854e-01 3.60744357e-01 -1.40133291e-01 -6.27180040e-01 3.94491911e-01 1.44001827e-01 4.01886344e-01 5.21875657e-02 -5.10810435e-01 -3.26340437e-01 -7.50520527e-01 -3.09733331e-01 -2.87999004e-01 -4.46975604e-02 -3.55256796e-01 3.96209776e-01 3.84051293e-01 -2.01183987e+00 7.88118780e-01 1.50532603e-01 1.63253403e+00 -2.04889521e-01 9.08549309e-01 -2.51964420e-01 1.05323255e+00 -7.78448939e-01 -4.67584640e-01 -1.83658898e-01 -3.98061484e-01 -6.08407617e-01 -3.52325290e-01 2.12783478e-02 -2.17285119e-02 -7.14202523e-02 9.39483762e-01 9.39125836e-01 3.72211307e-01 5.39109945e-01 -1.44922698e+00 -3.55856687e-01 4.47250366e-01 -4.69770789e-01 1.03238456e-01 5.85181236e-01 -4.31673914e-01 5.95763028e-01 -6.98544383e-01 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5b29bd82-4a0b-421d-ba82-78962ec73198	s2rl-do-we-really-need-to-perceive-all-states	2206.11054	null	https://arxiv.org/abs/2206.11054v1	https://arxiv.org/pdf/2206.11054v1.pdf	S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?	Collaborative multi-agent reinforcement learning (MARL) has been widely used in many practical applications, where each agent makes a decision based on its own observation. Most mainstream methods treat each local observation as an entirety when modeling the decentralized local utility functions. However, they ignore the fact that local observation information can be further divided into several entities, and only part of the entities is helpful to model inference. Moreover, the importance of different entities may change over time. To improve the performance of decentralized policies, the attention mechanism is used to capture features of local information. Nevertheless, existing attention models rely on dense fully connected graphs and cannot better perceive important states. To this end, we propose a sparse state based MARL (S2RL) framework, which utilizes a sparse attention mechanism to discard irrelevant information in local observations. The local utility functions are estimated through the self-attention and sparse attention mechanisms separately, then are combined into a standard joint value function and auxiliary joint value function in the central critic. We design the S2RL framework as a plug-and-play module, making it general enough to be applied to various methods. Extensive experiments on StarCraft II show that S2RL can significantly improve the performance of many state-of-the-art methods.	['Chao Wu', 'Yunfeng Shao', 'Furui Liu', 'Kun Kuang', 'Jiahui Li', 'Yinchuan Li', 'Shuang Luo']	2022-06-20	null	null	null	null	['starcraft-ii']	['playing-games']	[-7.08242893e-01 1.59900159e-01 -6.89445972e-01 -5.72386347e-02 -4.97884005e-01 -2.74834961e-01 4.49140012e-01 2.11196482e-01 -4.00130212e-01 9.57634091e-01 3.23042035e-01 1.76051542e-01 -1.13085248e-01 -7.95454204e-01 -5.66808701e-01 -1.05156684e+00 -3.89601700e-02 5.00269532e-01 4.43649828e-01 -4.06103045e-01 -2.44993702e-01 -5.72405756e-02 -8.17194581e-01 -2.81557113e-01 9.75933075e-01 8.08892488e-01 4.11786139e-01 3.68641555e-01 6.01224117e-02 1.48879409e+00 -6.78995609e-01 -4.75182198e-02 5.41027039e-02 -5.09107649e-01 -5.08062124e-01 3.61052006e-02 -3.24250102e-01 -5.77675283e-01 -6.71131849e-01 1.33425581e+00 6.06957614e-01 5.47826409e-01 2.50875443e-01 -1.39508605e+00 -8.70556772e-01 1.02838635e+00 -5.35537302e-01 2.05251589e-01 1.39953226e-01 5.02470255e-01 1.22895074e+00 -3.35673362e-01 3.71203393e-01 1.52784896e+00 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1ab10da0-5ae1-4b9b-8009-8556a99bf9b4	meta-meta-classification-for-one-shot	2004.08083	null	https://arxiv.org/abs/2004.08083v4	https://arxiv.org/pdf/2004.08083v4.pdf	Meta-Meta Classification for One-Shot Learning	We present a new approach, called meta-meta classification, to learning in small-data settings. In this approach, one uses a large set of learning problems to design an ensemble of learners, where each learner has high bias and low variance and is skilled at solving a specific type of learning problem. The meta-meta classifier learns how to examine a given learning problem and combine the various learners to solve the problem. The meta-meta learning approach is especially suited to solving few-shot learning tasks, as it is easier to learn to classify a new learning problem with little data than it is to apply a learning algorithm to a small data set. We evaluate the approach on a one-shot, one-class-versus-all classification task and show that it is able to outperform traditional meta-learning as well as ensembling approaches.	['Chris Jermaine', 'Dipak Chaudhari', 'Arkabandhu Chowdhury', 'Swarat Chaudhuri']	2020-04-17	null	null	null	null	['small-data']	['computer-vision']	[ 3.74900430e-01 1.33235931e-01 -4.06521767e-01 -3.43616456e-01 -1.21992397e+00 -2.72053689e-01 5.68353653e-01 5.35656869e-01 -5.24471164e-01 7.37158537e-01 -1.75669014e-01 -7.37532154e-02 -4.78682935e-01 -8.58081818e-01 -6.01451695e-01 -6.77547395e-01 2.96452232e-02 7.20871687e-01 1.96773320e-01 -3.21516067e-01 3.13646972e-01 -1.65607110e-01 -2.16045356e+00 7.59238064e-01 9.48242307e-01 7.37204313e-01 2.92162359e-01 8.84761989e-01 -4.39981282e-01 9.89029467e-01 -7.20346689e-01 -4.13299561e-01 8.77761096e-03 -7.36503303e-01 -1.17150819e+00 -1.56874508e-01 5.99801779e-01 3.91121417e-01 3.45716864e-01 9.57954645e-01 4.38460052e-01 5.15068173e-01 9.67432678e-01 -1.18225753e+00 -4.03744757e-01 5.94750643e-01 -4.72591251e-01 7.13233888e-01 2.74712861e-01 -1.05894901e-01 1.08939266e+00 -9.07995462e-01 3.85341346e-01 1.11325240e+00 8.74441206e-01 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c476d3b4-da04-49f7-8833-b8c42a12cae4	cognitive-compositional-semantics-using	null	null	https://aclanthology.info/papers/S14-1018/s14-1018	https://www.aclweb.org/anthology/S14-1018v2	Cognitive Compositional Semantics using Continuation Dependencies	null	['William Schuler', 'Adam Wheeler']	2014-08-01	cognitive-compositional-semantics-using-1	https://aclanthology.org/S14-1018	https://aclanthology.org/S14-1018.pdf	semeval-2014-8	['implicatures']	['natural-language-processing']	[-2.44508207e-01 3.89024585e-01 -2.65282035e-01 -2.15905145e-01 -8.60921741e-02 -7.76765764e-01 4.48510379e-01 -7.23253429e-01 -5.48377395e-01 1.31954515e+00 3.66348401e-02 -9.49533224e-01 -2.40340635e-01 -1.05564880e+00 -8.44053447e-01 -8.75781775e-01 -7.42435038e-01 6.86515033e-01 1.44298598e-01 -6.52004302e-01 8.47113907e-01 6.29996777e-01 -1.62287033e+00 5.77558100e-01 6.81926727e-01 5.49597681e-01 5.66466339e-02 1.04480565e+00 6.13576770e-02 1.59847176e+00 -6.05450153e-01 -4.56729174e-01 1.43710867e-01 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121133fe-1d27-497e-af07-a205ca29d3e5	structure-aware-incremental-learning-with	2305.01204	null	https://arxiv.org/abs/2305.01204v1	https://arxiv.org/pdf/2305.01204v1.pdf	Structure Aware Incremental Learning with Personalized Imitation Weights for Recommender Systems	Recommender systems now consume large-scale data and play a significant role in improving user experience. Graph Neural Networks (GNNs) have emerged as one of the most effective recommender system models because they model the rich relational information. The ever-growing volume of data can make training GNNs prohibitively expensive. To address this, previous attempts propose to train the GNN models incrementally as new data blocks arrive. Feature and structure knowledge distillation techniques have been explored to allow the GNN model to train in a fast incremental fashion while alleviating the catastrophic forgetting problem. However, preserving the same amount of the historical information for all users is sub-optimal since it fails to take into account the dynamics of each user's change of preferences. For the users whose interests shift substantially, retaining too much of the old knowledge can overly constrain the model, preventing it from quickly adapting to the users' novel interests. In contrast, for users who have static preferences, model performance can benefit greatly from preserving as much of the user's long-term preferences as possible. In this work, we propose a novel training strategy that adaptively learns personalized imitation weights for each user to balance the contribution from the recent data and the amount of knowledge to be distilled from previous time periods. We demonstrate the effectiveness of learning imitation weights via a comparison on five diverse datasets for three state-of-art structure distillation based recommender systems. The performance shows consistent improvement over competitive incremental learning techninques.	['Mark Coates', 'Jianye Hao', 'Chen Ma', 'Ruiming Tang', 'Antonios Valkanas', 'Yingxue Zhang', 'Yuening Wang']	2023-05-02	null	null	null	null	['incremental-learning']	['methodology']	[-2.75485992e-01 1.73015505e-01 -4.59245265e-01 -1.59016132e-01 8.68990049e-02 -5.03976166e-01 3.44423860e-01 2.12799147e-01 -3.97370458e-01 6.63043559e-01 4.84630525e-01 -1.12198986e-01 -3.86845887e-01 -1.01543605e+00 -6.30101025e-01 -3.81208628e-01 -1.68922961e-01 6.66346312e-01 3.34108531e-01 -8.14047635e-01 1.85210761e-02 5.10510921e-01 -1.57850862e+00 -4.28208383e-03 1.21845210e+00 6.66070044e-01 3.74728471e-01 3.61602336e-01 -1.76525712e-01 7.28361905e-01 -2.05839053e-01 -3.08277100e-01 4.56687242e-01 -2.65521824e-01 -4.81740236e-01 -3.16862494e-01 3.57540816e-01 -4.42983270e-01 -9.22002077e-01 6.52087033e-01 4.04360563e-01 8.10384929e-01 1.54963508e-01 -9.22505021e-01 -1.10045385e+00 1.00600100e+00 -2.15905666e-01 1.86937213e-01 2.00881109e-01 -1.25355870e-01 9.96065497e-01 -6.08690202e-01 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089b219e-e384-4bf7-a24e-f67ef3fa1c72	the-art-of-transfer-learning-an-adaptive-and	2305.00520	null	https://arxiv.org/abs/2305.00520v1	https://arxiv.org/pdf/2305.00520v1.pdf	The ART of Transfer Learning: An Adaptive and Robust Pipeline	Transfer learning is an essential tool for improving the performance of primary tasks by leveraging information from auxiliary data resources. In this work, we propose Adaptive Robust Transfer Learning (ART), a flexible pipeline of performing transfer learning with generic machine learning algorithms. We establish the non-asymptotic learning theory of ART, providing a provable theoretical guarantee for achieving adaptive transfer while preventing negative transfer. Additionally, we introduce an ART-integrated-aggregating machine that produces a single final model when multiple candidate algorithms are considered. We demonstrate the promising performance of ART through extensive empirical studies on regression, classification, and sparse learning. We further present a real-data analysis for a mortality study.	['Chenglong Ye', 'Yunan Wu', 'Boxiang Wang']	2023-04-30	null	null	null	null	['sparse-learning']	['methodology']	[ 2.94449806e-01 5.15967682e-02 -6.92981303e-01 -3.27985644e-01 -1.63865077e+00 -4.10169721e-01 4.10900712e-01 -4.39663120e-02 -3.13309669e-01 9.42592442e-01 4.53431308e-02 -4.63307619e-01 -3.08313429e-01 -3.92635584e-01 -1.29396141e+00 -5.75733125e-01 -4.83962059e-01 5.25086522e-01 -1.08830519e-01 9.86874476e-02 -2.25101574e-03 2.59587079e-01 -9.33149636e-01 6.88388050e-01 9.04463530e-01 1.05859101e+00 -2.83879995e-01 6.78033590e-01 3.81690234e-01 8.37712824e-01 -2.21591428e-01 -5.66813648e-01 4.98308182e-01 -4.98788834e-01 -6.83587790e-01 -1.87938660e-01 6.70976102e-01 -3.79403770e-01 -2.11367980e-01 7.28148043e-01 6.98335767e-01 1.18075639e-01 1.12387133e+00 -1.54598916e+00 -5.90925872e-01 6.94603503e-01 -6.02201045e-01 2.54856914e-01 -9.89591554e-02 1.22447006e-01 1.03416049e+00 -1.23083508e+00 2.54567176e-01 1.19008553e+00 1.17340577e+00 7.18164623e-01 -1.22301435e+00 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3.69897187e-01 6.16889656e-01 -5.01071513e-01 -5.31256557e-01 2.74734229e-01]	[9.603504180908203, 3.0564982891082764]
9d2fac36-1b71-4ae8-9642-356fec7196ac	disease-oriented-image-embedding-with-pseudo	2108.06518	null	https://arxiv.org/abs/2108.06518v1	https://arxiv.org/pdf/2108.06518v1.pdf	Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI	To build a robust and practical content-based image retrieval (CBIR) system that is applicable to a clinical brain MRI database, we propose a new framework -- Disease-oriented image embedding with pseudo-scanner standardization (DI-PSS) -- that consists of two core techniques, data harmonization and a dimension reduction algorithm. Our DI-PSS uses skull stripping and CycleGAN-based image transformations that map to a standard brain followed by transformation into a brain image taken with a given reference scanner. Then, our 3D convolutioinal autoencoders (3D-CAE) with deep metric learning acquires a low-dimensional embedding that better reflects the characteristics of the disease. The effectiveness of our proposed framework was tested on the T1-weighted MRIs selected from the Alzheimer's Disease Neuroimaging Initiative and the Parkinson's Progression Markers Initiative. We confirmed that our PSS greatly reduced the variability of low-dimensional embeddings caused by different scanner and datasets. Compared with the baseline condition, our PSS reduced the variability in the distance from Alzheimer's disease (AD) to clinically normal (CN) and Parkinson disease (PD) cases by 15.8-22.6% and 18.0-29.9%, respectively. These properties allow DI-PSS to generate lower dimensional representations that are more amenable to disease classification. In AD and CN classification experiments based on spectral clustering, PSS improved the average accuracy and macro-F1 by 6.2% and 10.7%, respectively. Given the potential of the DI-PSS for harmonizing images scanned by MRI scanners that were not used to scan the training data, we expect that the DI-PSS is suitable for application to a large number of legacy MRIs scanned in heterogeneous environments.	['Kenichi Oishi', 'Hitoshi Iyatomi', 'Yusuke Chayama', 'Kumpei Ikuta', 'Yuto Onga', 'Hayato Arai']	2021-08-14	null	null	null	null	['content-based-image-retrieval', 'skull-stripping']	['computer-vision', 'medical']	[ 2.96354860e-01 -2.92104464e-02 3.07034433e-01 -4.83357221e-01 -8.72164965e-01 -1.66459858e-01 5.21856844e-01 -1.95182160e-01 -6.73646450e-01 4.35180336e-01 3.60537887e-01 1.26608163e-01 -4.05199677e-01 -6.57677174e-01 -2.46758103e-01 -7.92329013e-01 -4.73002017e-01 6.27146065e-01 2.02015817e-01 -7.62297288e-02 -1.04722477e-01 6.65273011e-01 -1.20538855e+00 3.23357433e-01 5.89065790e-01 9.57704663e-01 5.63051820e-01 2.92689532e-01 8.34657997e-02 2.61556000e-01 -4.97598052e-01 -1.53217033e-01 4.77689177e-01 -2.15761825e-01 -5.75549781e-01 -7.49941031e-03 2.65992761e-01 -4.99652624e-01 -4.85792816e-01 1.09768546e+00 9.14442837e-01 -2.34415397e-01 9.17456508e-01 -1.09650409e+00 -8.90544295e-01 3.40382665e-01 -6.09598517e-01 4.55091000e-01 -2.55536407e-01 5.22929542e-02 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8e1d7111-4aff-4172-a9ec-32e7f6bd6356	searching-efficient-3d-architectures-with	2007.16100	null	https://arxiv.org/abs/2007.16100v2	https://arxiv.org/pdf/2007.16100v2.pdf	Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution	Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely. Given the limited hardware resources, existing 3D perception models are not able to recognize small instances (e.g., pedestrians, cyclists) very well due to the low-resolution voxelization and aggressive downsampling. To this end, we propose Sparse Point-Voxel Convolution (SPVConv), a lightweight 3D module that equips the vanilla Sparse Convolution with the high-resolution point-based branch. With negligible overhead, this point-based branch is able to preserve the fine details even from large outdoor scenes. To explore the spectrum of efficient 3D models, we first define a flexible architecture design space based on SPVConv, and we then present 3D Neural Architecture Search (3D-NAS) to search the optimal network architecture over this diverse design space efficiently and effectively. Experimental results validate that the resulting SPVNAS model is fast and accurate: it outperforms the state-of-the-art MinkowskiNet by 3.3%, ranking 1st on the competitive SemanticKITTI leaderboard. It also achieves 8x computation reduction and 3x measured speedup over MinkowskiNet with higher accuracy. Finally, we transfer our method to 3D object detection, and it achieves consistent improvements over the one-stage detection baseline on KITTI.	['Song Han', 'Yujun Lin', 'Haotian Tang', 'Hanrui Wang', 'Shengyu Zhao', 'Zhijian Liu', 'Ji Lin']	2020-07-31	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/6421_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123730681.pdf	eccv-2020-8	['robust-3d-semantic-segmentation', 'lidar-semantic-segmentation']	['computer-vision', 'computer-vision']	[-2.10286483e-01 5.47750182e-02 -2.56315649e-01 -3.82222831e-01 -5.19641221e-01 -3.55022728e-01 5.31485260e-01 -3.03729653e-01 -2.40347683e-01 6.43245578e-02 -2.51966178e-01 -7.17547119e-01 2.38266259e-01 -8.69923949e-01 -1.05811977e+00 -2.40236655e-01 2.26295236e-02 5.89130580e-01 8.73901010e-01 -3.42460811e-01 4.57554348e-02 9.59423482e-01 -1.75063050e+00 3.14484805e-01 6.54492140e-01 1.35761285e+00 3.11846435e-01 5.53727508e-01 -3.23635101e-01 5.24059594e-01 -3.55665833e-01 -8.18026736e-02 6.45339072e-01 3.63043159e-01 -3.98304015e-01 3.44928838e-02 1.11248636e+00 -7.62636244e-01 -5.83165467e-01 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926bdaa9-9e40-4a8f-b780-8d43a057def2	tetra-nerf-representing-neural-radiance	2304.09987	null	https://arxiv.org/abs/2304.09987v2	https://arxiv.org/pdf/2304.09987v2.pdf	Tetra-NeRF: Representing Neural Radiance Fields Using Tetrahedra	Neural Radiance Fields (NeRFs) are a very recent and very popular approach for the problems of novel view synthesis and 3D reconstruction. A popular scene representation used by NeRFs is to combine a uniform, voxel-based subdivision of the scene with an MLP. Based on the observation that a (sparse) point cloud of the scene is often available, this paper proposes to use an adaptive representation based on tetrahedra obtained by the Delaunay triangulation instead of the uniform subdivision or point-based representations. We show that such a representation enables efficient training and leads to state-of-the-art results. Our approach elegantly combines concepts from 3D geometry processing, triangle-based rendering, and modern neural radiance fields. Compared to voxel-based representations, ours provides more detail around parts of the scene likely to be close to the surface. Compared to point-based representations, our approach achieves better performance.	['Torsten Sattler', 'Jonas Kulhanek']	2023-04-19	null	null	null	null	['3d-reconstruction', 'novel-view-synthesis']	['computer-vision', 'computer-vision']	[ 3.21815461e-01 -7.28047341e-02 4.55656379e-01 -4.06634301e-01 -6.20437264e-01 -2.95537233e-01 7.51389980e-01 1.72410235e-01 -1.20986111e-01 4.36429769e-01 1.14634804e-01 -2.32603535e-01 -1.96857631e-01 -1.45074821e+00 -9.24978912e-01 -5.92185855e-01 7.62766227e-02 5.81677675e-01 8.33599195e-02 -5.26568949e-01 3.59751672e-01 1.27454662e+00 -1.96168411e+00 4.97259408e-01 6.49200261e-01 1.05161548e+00 2.23929882e-01 6.37157977e-01 -4.42218333e-01 5.58093131e-01 -3.20277989e-01 6.98728710e-02 3.81975979e-01 3.30905430e-02 -6.54810727e-01 -1.14278987e-01 8.92171443e-01 -1.72947854e-01 -6.78362995e-02 7.29089797e-01 3.49762470e-01 5.36101282e-01 7.58823037e-01 -6.18706882e-01 -3.43726128e-01 3.65705267e-02 -6.78781986e-01 -2.28672296e-01 4.53347772e-01 -2.85835892e-01 6.54819846e-01 -1.12860358e+00 6.08546972e-01 1.32300448e+00 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e5e6ddbf-2a6f-4fd1-a992-67ae781313c7	a-weak-supervision-approach-for-predicting	null	null	https://aclanthology.org/2022.coling-1.400	https://aclanthology.org/2022.coling-1.400.pdf	A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions	Predicting difficulty of questions is crucial for technical interviews. However, such questions are long-form and more open-ended than factoid and multiple choice questions explored so far for question difficulty prediction. Existing models also require large volumes of candidate response data for training. We study weak-supervision and use unsupervised algorithms for both question generation and difficulty prediction. We create a dataset of interview questions with difficulty scores for deep learning and use it to evaluate SOTA models for question difficulty prediction trained using weak supervision. Our analysis brings out the task’s difficulty as well as the promise of weak supervision for it.	['Indrajit Bhattacharya', 'Tapas Nayak', 'Pratik Saini', 'Subhasish Ghosh', 'Arpita Kundu']	null	null	null	null	coling-2022-10	['question-generation']	['natural-language-processing']	[ 5.02263494e-02 8.56827557e-01 -6.25051409e-02 -8.48228455e-01 -1.30875766e+00 -8.24225366e-01 1.79709822e-01 2.82466054e-01 -2.75855392e-01 4.91910875e-01 8.92888784e-01 -8.49452138e-01 -3.84930581e-01 -5.00789702e-01 -1.94591388e-01 2.55140096e-01 4.25065041e-01 8.05842221e-01 3.24213095e-02 -6.71146154e-01 1.26491860e-01 -2.61954576e-01 -9.38073337e-01 5.44258058e-01 1.03863442e+00 9.33392823e-01 3.64839494e-01 1.06488383e+00 -5.69997787e-01 1.29800093e+00 -7.01916873e-01 -6.92077756e-01 -6.44576475e-02 -3.26733619e-01 -1.81493652e+00 1.91283301e-02 6.92639172e-01 -6.67930961e-01 -4.05574292e-01 3.37765008e-01 4.42002386e-01 1.68362573e-01 5.06686509e-01 -1.01235950e+00 -7.96449006e-01 8.17344248e-01 2.66254306e-01 4.30715889e-01 9.76993024e-01 9.49453041e-02 1.52033484e+00 -8.93869162e-01 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5ee46b03-0657-4ad8-8e89-1fa4e2b3efb0	c-norm-a-neural-approach-to-few-shot-entity	null	null	https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03886-8	https://bmcbioinformatics.biomedcentral.com/counter/pdf/10.1186/s12859-020-03886-8.pdf	C-Norm: a neural approach to few-shot entity normalization	Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the latest machine learning-based approaches, which have difficulty handling highly multi-class and few-shot learning problems. To address this issue, we propose C-Norm, a new neural approach which synergistically combines standard and weak supervision, ontological knowledge integration and distributional semantics.	['Claire Nédellec', 'Pierre Zweigenbaum', 'Robert Bossy', 'Louise Deléger', 'Arnaud Ferré']	2020-12-29	null	null	null	bmc-bioinformatics-2020-12	['medical-concept-normalization']	['medical']	[ 3.47591788e-01 -4.73058186e-02 -3.90293032e-01 -3.56802166e-01 -2.10838839e-01 -5.25668785e-02 5.03447056e-01 7.29846895e-01 -9.70735669e-01 1.10205078e+00 2.60888994e-01 -1.03826895e-02 -3.38807762e-01 -7.64681816e-01 -2.98813749e-02 -5.90286195e-01 1.10106103e-01 2.11260930e-01 6.14852846e-01 -4.23408359e-01 1.69939250e-01 4.78181064e-01 -1.62861907e+00 -3.74251194e-02 8.44919264e-01 6.82332873e-01 -2.28963032e-01 1.27639011e-01 -4.35531080e-01 6.70244396e-01 -2.96673059e-01 -7.64644146e-01 -1.10760055e-01 -1.11034892e-01 -7.72457063e-01 -5.53140581e-01 3.76763456e-02 3.20592910e-01 -1.59032956e-01 1.53335690e+00 7.59274960e-01 4.78542060e-01 6.70825899e-01 -1.11080229e+00 -6.38866961e-01 4.29161370e-01 -6.87939644e-01 4.51237351e-01 9.37381387e-02 -2.31631413e-01 9.39968467e-01 -8.79083812e-01 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b1e326b2-70fc-411f-8ca4-87e55197cd55	transform-invariant-convolutional-neural-1	2206.13388	null	https://arxiv.org/abs/2206.13388v1	https://arxiv.org/pdf/2206.13388v1.pdf	Transform-Invariant Convolutional Neural Networks for Image Classification and Search	This paper demonstrates that a simple modification of the variational autoencoder (VAE) formalism enables the method to identify and classify rotated and distorted digits. In particular, the conventional objective (cost) function employed during the training process of a VAE both quantifies the agreement between the input and output data records and ensures that the latent space representation of the input data record is statistically generated with an appropriate mean and standard deviation. After training, simulated data realizations are generated by decoding appropriate latent space points. Since, however, standard VAE:s trained on randomly rotated MNIST digits cannot reliably distinguish between different digit classes since the rotated input data is effectively compared to a similarly rotated output data record. In contrast, an alternative implementation in which the objective function compares the output associated with each rotated digit to a corresponding fixed unreferenced reference digit is shown here to discriminate accurately among the rotated digits in latent space even when the dimension of the latent space is 2 or 3.	['David Yevick']	2022-06-18	null	null	null	null	['rotated-mnist']	['computer-vision']	[ 3.56446028e-01 1.34451136e-01 7.83998370e-02 -3.08783233e-01 -8.00567627e-01 -8.17889035e-01 9.42967594e-01 -3.46549213e-01 -3.40315163e-01 8.00557554e-01 1.73572302e-01 -1.53040346e-02 -1.72257647e-01 -6.24437034e-01 -4.72857118e-01 -1.10186756e+00 2.68349230e-01 6.53389633e-01 -5.28616428e-01 1.69869438e-01 1.39097258e-01 6.13730550e-01 -1.64617217e+00 1.25022516e-01 4.89839375e-01 8.51842105e-01 4.63189147e-02 8.95121694e-01 1.30362988e-01 3.24245572e-01 -1.18052590e+00 -2.99131215e-01 4.01766837e-01 -8.14423800e-01 -4.19122487e-01 1.81544438e-01 4.68959928e-01 -1.14262082e-01 -4.65896070e-01 1.40067136e+00 4.20183331e-01 3.03970963e-01 1.26565504e+00 -1.08074450e+00 -9.47513223e-01 5.84847271e-01 1.16536736e-01 -1.83586925e-02 2.66295522e-01 -2.24076845e-02 7.76789248e-01 -6.28556788e-01 6.82912588e-01 1.18698168e+00 4.06255543e-01 7.43335545e-01 -1.79177034e+00 -5.36987603e-01 -2.69527823e-01 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377bb1ff-2057-4108-b511-e96c1c31b060	adversarial-attack-based-on-prediction	2306.01809	null	https://arxiv.org/abs/2306.01809v1	https://arxiv.org/pdf/2306.01809v1.pdf	Adversarial Attack Based on Prediction-Correction	Deep neural networks (DNNs) are vulnerable to adversarial examples obtained by adding small perturbations to original examples. The added perturbations in existing attacks are mainly determined by the gradient of the loss function with respect to the inputs. In this paper, the close relationship between gradient-based attacks and the numerical methods for solving ordinary differential equation (ODE) is studied for the first time. Inspired by the numerical solution of ODE, a new prediction-correction (PC) based adversarial attack is proposed. In our proposed PC-based attack, some existing attack can be selected to produce a predicted example first, and then the predicted example and the current example are combined together to determine the added perturbations. The proposed method possesses good extensibility and can be applied to all available gradient-based attacks easily. Extensive experiments demonstrate that compared with the state-of-the-art gradient-based adversarial attacks, our proposed PC-based attacks have higher attack success rates, and exhibit better transferability.	['Fangjun Huang', 'Chen Wan']	2023-06-02	null	null	null	null	['adversarial-attack']	['adversarial']	[ 1.79067533e-02 -9.80727300e-02 2.19933480e-01 -4.24514413e-02 -3.95202786e-01 -5.96031368e-01 4.48072463e-01 -1.44796655e-01 -5.79041004e-01 7.77775645e-01 -2.84968257e-01 -3.35749775e-01 -5.73878409e-03 -8.87884021e-01 -6.64806426e-01 -9.79052484e-01 -1.15594506e-01 -7.83044174e-02 6.07029319e-01 -6.67346239e-01 2.01947317e-01 9.15696263e-01 -8.67632926e-01 -1.32289231e-01 9.77338612e-01 9.92734373e-01 -2.27029547e-01 5.83132684e-01 -1.08950231e-02 7.73985744e-01 -8.57062161e-01 -6.91290319e-01 6.89284027e-01 -4.56005514e-01 -4.34691638e-01 -4.78390694e-01 2.78215885e-01 -4.27221268e-01 -8.85020554e-01 1.50530887e+00 8.64527285e-01 1.64200276e-01 5.56661904e-01 -1.55604780e+00 -4.97314602e-01 5.03542364e-01 -2.41145551e-01 3.41326326e-01 4.61850651e-02 1.81335509e-01 4.14054185e-01 -8.05486023e-01 2.40723535e-01 1.37112737e+00 8.77161205e-01 1.08932304e+00 -7.66293049e-01 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3.78264219e-01 4.59266782e-01 -5.11249363e-01 -1.13514042e+00 -3.90768081e-01]	[5.57551383972168, 7.960184574127197]
53116d69-2b2c-4fe8-8b7f-19152ad9803b	a-family-of-cognitively-realistic-parsing	null	null	https://openreview.net/forum?id=YKrSi_BzRh	https://openreview.net/pdf?id=YKrSi_BzRh	A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning	The hierarchical syntactic structure of natural language is a key feature of human cognition that enables us to recursively construct arbitrarily long sentences supporting communication of complex, relational information. In this work, we describe a framework in which learning cognitively-realistic left-corner parsers can be formalized as a Reinforcement Learning problem, and introduce a family of cognitively realistic chart-parsing environments to evaluate potential psycholinguistic implications of RL algorithms. We report how several baseline Q-learning and Actor Critic algorithms, both tabular and neural, perform on subsets of the Penn Treebank corpus. We observe a sharp increase in difficulty as parse trees get slightly more complex, indicating that hierarchical reinforcement learning might be required to solve this family of environments.	['Anonymous']	2022-01-16	null	null	null	acl-arr-january-2022-1	['hierarchical-reinforcement-learning']	['methodology']	[-6.79238141e-03 9.44461703e-01 2.29479000e-01 -4.48507935e-01 -1.01370454e+00 -8.74322593e-01 3.63631874e-01 2.49182597e-01 -5.64168990e-01 1.02092636e+00 5.17937005e-01 -9.42945719e-01 -2.14451730e-01 -8.14086139e-01 -6.54627800e-01 -1.34801015e-01 -3.47792685e-01 5.81166744e-01 2.44521081e-01 -6.01087391e-01 2.74759620e-01 4.64488029e-01 -1.18245649e+00 3.03203642e-01 7.18565166e-01 3.14323157e-01 4.81989652e-01 1.16749871e+00 -2.23394334e-01 1.33279860e+00 -7.16291010e-01 -5.92957377e-01 -5.36358766e-02 -4.50780064e-01 -1.33674455e+00 9.42499936e-02 1.66770637e-01 -2.34162405e-01 -1.29082143e-01 9.15175319e-01 3.85760486e-01 2.29511201e-01 4.23847258e-01 -5.81533670e-01 -5.36417067e-01 1.11861932e+00 -7.92935789e-02 5.26518285e-01 6.48488700e-01 2.66768605e-01 1.43525207e+00 -2.75588781e-01 5.61244428e-01 1.85125411e+00 4.22812194e-01 6.84209406e-01 -1.25022733e+00 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2.04049781e-01 1.90754697e-01 -4.02203143e-01 -1.10992283e-01 -1.45388827e-01]	[4.072351932525635, 1.387505054473877]
edcdea0f-9054-447d-9884-17d5294d9a8d	deep-person-generation-a-survey-from-the	2109.02081	null	https://arxiv.org/abs/2109.02081v1	https://arxiv.org/pdf/2109.02081v1.pdf	Deep Person Generation: A Survey from the Perspective of Face, Pose and Cloth Synthesis	Deep person generation has attracted extensive research attention due to its wide applications in virtual agents, video conferencing, online shopping and art/movie production. With the advancement of deep learning, visual appearances (face, pose, cloth) of a person image can be easily generated or manipulated on demand. In this survey, we first summarize the scope of person generation, and then systematically review recent progress and technical trends in deep person generation, covering three major tasks: talking-head generation (face), pose-guided person generation (pose) and garment-oriented person generation (cloth). More than two hundred papers are covered for a thorough overview, and the milestone works are highlighted to witness the major technical breakthrough. Based on these fundamental tasks, a number of applications are investigated, e.g., virtual fitting, digital human, generative data augmentation. We hope this survey could shed some light on the future prospects of deep person generation, and provide a helpful foundation for full applications towards digital human.	['Tao Mei', 'Zhoujun Li', 'Tong Shen', 'Wei zhang', 'Tong Sha']	2021-09-05	null	null	null	null	['talking-head-generation']	['computer-vision']	[ 3.86007190e-01 4.63327974e-01 2.75779843e-01 -2.60730833e-01 -1.51449472e-01 -3.05481583e-01 7.59863734e-01 -7.05114305e-01 1.85241908e-01 8.61006498e-01 2.27525339e-01 3.78245264e-01 2.76787460e-01 -8.36312830e-01 -4.87842441e-01 -8.23707283e-01 8.12270194e-02 5.97100854e-01 -4.43407923e-01 -4.38578814e-01 -1.69903010e-01 5.39560080e-01 -1.98992467e+00 9.15991068e-02 5.48301399e-01 7.59605110e-01 1.21849969e-01 7.74874091e-01 1.34247035e-01 3.51972759e-01 -1.12870181e+00 -9.93949115e-01 -3.21068726e-02 -4.96337980e-01 -5.32555521e-01 7.48101473e-01 3.83211017e-01 -5.49853921e-01 -5.42429805e-01 7.31071532e-01 1.29321563e+00 3.66621345e-01 7.35724270e-01 -1.68357909e+00 -1.00280833e+00 3.52905989e-01 -5.74941635e-01 -4.64899600e-01 1.00239658e+00 1.69009671e-01 3.25629890e-01 -9.32449818e-01 8.59194160e-01 1.71754861e+00 6.61120236e-01 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b433bae6-460b-4ec3-a742-7efe811313cf	multi-label-image-classification-with	1612.01082	null	http://arxiv.org/abs/1612.01082v3	http://arxiv.org/pdf/1612.01082v3.pdf	Multi-Label Image Classification with Regional Latent Semantic Dependencies	Deep convolution neural networks (CNN) have demonstrated advanced performance on single-label image classification, and various progress also have been made to apply CNN methods on multi-label image classification, which requires to annotate objects, attributes, scene categories etc. in a single shot. Recent state-of-the-art approaches to multi-label image classification exploit the label dependencies in an image, at global level, largely improving the labeling capacity. However, predicting small objects and visual concepts is still challenging due to the limited discrimination of the global visual features. In this paper, we propose a Regional Latent Semantic Dependencies model (RLSD) to address this problem. The utilized model includes a fully convolutional localization architecture to localize the regions that may contain multiple highly-dependent labels. The localized regions are further sent to the recurrent neural networks (RNN) to characterize the latent semantic dependencies at the regional level. Experimental results on several benchmark datasets show that our proposed model achieves the best performance compared to the state-of-the-art models, especially for predicting small objects occurred in the images. In addition, we set up an upper bound model (RLSD+ft-RPN) using bounding box coordinates during training, the experimental results also show that our RLSD can approach the upper bound without using the bounding-box annotations, which is more realistic in the real world.	['Jun-Jie Zhang', 'Chunhua Shen', 'Qi Wu', 'Jianfeng Lu', 'Jian Zhang']	2016-12-04	null	null	null	null	['multi-label-image-classification']	['computer-vision']	[ 1.92564085e-01 -3.65968257e-01 -3.91812533e-01 -4.71847087e-01 -7.05043256e-01 -3.03661436e-01 2.59768814e-01 2.05793485e-01 -3.20624352e-01 4.51249719e-01 -9.65485070e-03 3.96682695e-02 1.06525056e-01 -5.61152041e-01 -6.01705194e-01 -8.84935737e-01 2.40396932e-01 2.50635147e-01 3.62882346e-01 2.99498230e-01 2.59914637e-01 4.20682132e-01 -1.79891741e+00 8.11982930e-01 3.61067206e-01 1.40932047e+00 3.82766157e-01 3.20386678e-01 -3.21738183e-01 1.33853471e+00 -5.57411373e-01 8.27484652e-02 -1.16543032e-01 -3.05536717e-01 -8.80631745e-01 4.33078408e-02 4.85949218e-01 -1.81157932e-01 9.26024839e-02 1.14074337e+00 3.19436520e-01 9.18837339e-02 7.69923329e-01 -1.22443295e+00 -8.05021882e-01 2.91000098e-01 -7.22160876e-01 1.15692511e-01 -1.32021055e-01 -4.56475347e-01 1.01379371e+00 -9.17572021e-01 4.73048687e-01 1.25923800e+00 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d47bb08a-5098-4f88-8aa3-702004dd7f89	irrgn-an-implicit-relational-reasoning-graph	2212.00482	null	https://arxiv.org/abs/2212.00482v1	https://arxiv.org/pdf/2212.00482v1.pdf	IRRGN: An Implicit Relational Reasoning Graph Network for Multi-turn Response Selection	The task of response selection in multi-turn dialogue is to find the best option from all candidates. In order to improve the reasoning ability of the model, previous studies pay more attention to using explicit algorithms to model the dependencies between utterances, which are deterministic, limited and inflexible. In addition, few studies consider differences between the options before and after reasoning. In this paper, we propose an Implicit Relational Reasoning Graph Network to address these issues, which consists of the Utterance Relational Reasoner (URR) and the Option Dual Comparator (ODC). URR aims to implicitly extract dependencies between utterances, as well as utterances and options, and make reasoning with relational graph convolutional networks. ODC focuses on perceiving the difference between the options through dual comparison, which can eliminate the interference of the noise options. Experimental results on two multi-turn dialogue reasoning benchmark datasets MuTual and MuTual+ show that our method significantly improves the baseline of four pretrained language models and achieves state-of-the-art performance. The model surpasses human performance for the first time on the MuTual dataset.	['Wei Peng', 'Yuanchen Ju', 'Xuewei Guo', 'Hengwei Dai', 'Jingcheng Deng']	2022-12-01	null	null	null	null	['relational-reasoning']	['natural-language-processing']	[ 5.70777804e-02 7.57327199e-01 -2.13490482e-02 -7.74363577e-01 -6.33492053e-01 -5.22499502e-01 6.18104875e-01 1.81851424e-02 -3.06567103e-01 3.53996843e-01 8.11057031e-01 -6.79221988e-01 1.07853092e-01 -9.12112772e-01 -3.59988540e-01 -1.80128068e-01 5.59801221e-01 8.81511271e-01 2.66889304e-01 -9.50382173e-01 1.04278713e-01 -6.95618913e-02 -8.30472231e-01 9.83950377e-01 8.66269052e-01 7.39655912e-01 2.53089401e-03 6.23774886e-01 -6.82970166e-01 1.52860630e+00 -5.97762227e-01 -8.00402403e-01 -1.83773562e-01 -7.64567316e-01 -1.52298427e+00 -2.12075144e-01 -1.94200084e-01 -6.72387123e-01 -3.45136672e-01 7.02122033e-01 4.96363550e-01 3.29511583e-01 4.89072382e-01 -1.04807186e+00 -9.82545078e-01 1.68847847e+00 2.02424079e-02 -1.54179692e-01 9.09171104e-01 3.33885968e-01 1.43297672e+00 -8.17896068e-01 4.54671413e-01 1.85148263e+00 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b77d6b3f-2cc8-41f3-ac76-ff0c070b1210	edvr-video-restoration-with-enhanced	1905.02716	null	https://arxiv.org/abs/1905.02716v1	https://arxiv.org/pdf/1905.02716v1.pdf	EDVR: Video Restoration with Enhanced Deformable Convolutional Networks	Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing attention in the computer vision community. A challenging benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark challenges existing methods from two aspects: (1) how to align multiple frames given large motions, and (2) how to effectively fuse different frames with diverse motion and blur. In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges. First, to handle large motions, we devise a Pyramid, Cascading and Deformable (PCD) alignment module, in which frame alignment is done at the feature level using deformable convolutions in a coarse-to-fine manner. Second, we propose a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration. Thanks to these modules, our EDVR wins the champions and outperforms the second place by a large margin in all four tracks in the NTIRE19 video restoration and enhancement challenges. EDVR also demonstrates superior performance to state-of-the-art published methods on video super-resolution and deblurring. The code is available at https://github.com/xinntao/EDVR.	['Chen Change Loy', 'Xintao Wang', 'Kelvin C. K. Chan', 'Chao Dong', 'Ke Yu']	2019-05-07	null	null	null	null	['video-enhancement', 'video-restoration']	['computer-vision', 'computer-vision']	[ 0.1663237 -0.77225274 0.01562331 0.00757188 -0.6237472 -0.36257818 0.44273847 -0.63179207 -0.4001946 0.74202716 0.7526974 0.18791288 -0.10236313 -0.42022565 -0.686941 -0.75569934 0.186762 -0.32034642 0.43926764 -0.42899647 0.16191009 0.45313445 -1.4400182 0.39569834 0.9468335 0.7029963 0.42426875 0.7430958 0.40509734 0.96174806 -0.10004152 -0.19094199 0.26466525 -0.32875615 -0.8823795 0.12834601 0.7798835 -0.87371236 -0.8509393 1.2250686 0.64638746 0.62439984 0.2871469 -0.9173361 -1.192015 0.40862444 -0.83598727 0.8666705 0.37265688 0.26484022 0.5614132 -1.0435193 0.59276456 1.3636466 0.60707885 0.69681907 -1.044128 -0.6124975 0.29742682 0.6274232 -1.2810514 -0.7220254 0.4687952 -0.49982488 0.7335929 0.2940208 0.25632536 1.0788385 0.06529114 0.59944147 0.87659466 0.01826376 -0.18428203 -0.5832769 0.0536764 0.29611334 -0.02308415 0.2701024 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942fba5e-73a3-4d39-8046-50537acf249a	crowdsourcing-cybersecurity-cyber-attack	1702.07745	null	http://arxiv.org/abs/1702.07745v1	http://arxiv.org/pdf/1702.07745v1.pdf	Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media	Social media is often viewed as a sensor into various societal events such as disease outbreaks, protests, and elections. We describe the use of social media as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our approach detects a broad range of cyber-attacks (e.g., distributed denial of service (DDOS) attacks, data breaches, and account hijacking) in an unsupervised manner using just a limited fixed set of seed event triggers. A new query expansion strategy based on convolutional kernels and dependency parses helps model reporting structure and aids in identifying key event characteristics. Through a large-scale analysis over Twitter, we demonstrate that our approach consistently identifies and encodes events, outperforming existing methods.	['Ramakrishnan Naren', 'Lu Chang-Tien', 'Wang Gang', 'Jan Steve', 'Ji Taoran', 'Khandpur Rupinder Paul']	2017-02-24	null	null	null	null	['cyber-attack-detection']	['miscellaneous']	[-1.49986625e-01 -5.09807616e-02 -3.13060582e-01 -2.40440547e-01 -7.32393265e-01 -1.10977077e+00 9.00438547e-01 1.32056618e+00 -3.05421531e-01 5.25669456e-01 4.70052153e-01 -3.77287120e-01 2.89591640e-01 -1.34917045e+00 -5.44587612e-01 1.87742472e-01 -3.61943096e-01 4.55594867e-01 7.41163194e-01 -3.34465504e-01 1.74800619e-01 7.46435761e-01 -7.10049808e-01 1.02705926e-01 3.22202027e-01 8.60927761e-01 -8.20820510e-01 5.64739227e-01 -2.48496652e-01 7.06728280e-01 -1.11790586e+00 -8.60535622e-01 -4.09437902e-02 1.91815048e-01 -4.72278088e-01 -3.54333133e-01 -2.62230281e-02 -3.43058765e-01 -5.49188197e-01 1.02112293e+00 5.15410900e-01 -2.79600531e-01 2.88300198e-02 -1.32237351e+00 -4.93472338e-01 8.00751865e-01 -4.27393109e-01 9.35364366e-01 8.54207754e-01 1.69465423e-01 9.59366262e-01 -6.22392833e-01 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fe9df5cf-779f-4512-9f0b-e8a290dd5f26	on-transforming-reinforcement-learning-by	2212.14164	null	https://arxiv.org/abs/2212.14164v2	https://arxiv.org/pdf/2212.14164v2.pdf	On Transforming Reinforcement Learning by Transformer: The Development Trajectory	Transformer, originally devised for natural language processing, has also attested significant success in computer vision. Thanks to its super expressive power, researchers are investigating ways to deploy transformers to reinforcement learning (RL) and the transformer-based models have manifested their potential in representative RL benchmarks. In this paper, we collect and dissect recent advances on transforming RL by transformer (transformer-based RL or TRL), in order to explore its development trajectory and future trend. We group existing developments in two categories: architecture enhancement and trajectory optimization, and examine the main applications of TRL in robotic manipulation, text-based games, navigation and autonomous driving. For architecture enhancement, these methods consider how to apply the powerful transformer structure to RL problems under the traditional RL framework, which model agents and environments much more precisely than deep RL methods, but they are still limited by the inherent defects of traditional RL algorithms, such as bootstrapping and "deadly triad". For trajectory optimization, these methods treat RL problems as sequence modeling and train a joint state-action model over entire trajectories under the behavior cloning framework, which are able to extract policies from static datasets and fully use the long-sequence modeling capability of the transformer. Given these advancements, extensions and challenges in TRL are reviewed and proposals about future direction are discussed. We hope that this survey can provide a detailed introduction to TRL and motivate future research in this rapidly developing field.	['DaCheng Tao', 'Yixin Chen', 'Ya zhang', 'Li Shen', 'Shengchao Hu']	2022-12-29	null	null	null	null	['text-based-games']	['playing-games']	[-2.51277722e-02 -2.47196872e-02 -3.76519322e-01 -1.03595786e-01 -4.89036828e-01 -9.05433893e-01 7.94848859e-01 -4.52680677e-01 -6.44059658e-01 6.32958770e-01 9.70405713e-02 -5.64904451e-01 -2.91678518e-01 -7.69093990e-01 -6.96043134e-01 -8.00277710e-01 -1.74560905e-01 6.41926825e-01 1.02436639e-01 -8.38291824e-01 2.32959300e-01 6.35006428e-01 -1.80528545e+00 1.19999878e-01 6.04296923e-01 7.25078046e-01 4.63226318e-01 6.98950410e-01 -1.69080958e-01 1.55096638e+00 -3.45689356e-01 -1.07340375e-03 2.26849034e-01 -2.71234691e-01 -8.86746585e-01 -2.71132678e-01 -1.73387960e-01 -3.75880629e-01 -8.22544634e-01 6.64652050e-01 4.16957736e-01 4.31335270e-01 3.86229336e-01 -1.68312085e+00 -4.32913035e-01 7.36418724e-01 -6.30682781e-02 -8.15896541e-02 2.52152652e-01 6.04781330e-01 9.30245161e-01 -4.82536703e-01 6.57505393e-01 1.40234065e+00 7.06323683e-01 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dafe45da-f9cb-4686-8b26-f1ded5103ac7	self-supervised-image-to-point-distillation	2301.05709	null	https://arxiv.org/abs/2301.05709v2	https://arxiv.org/pdf/2301.05709v2.pdf	Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss	An effective framework for learning 3D representations for perception tasks is distilling rich self-supervised image features via contrastive learning. However, image-to point representation learning for autonomous driving datasets faces two main challenges: 1) the abundance of self-similarity, which results in the contrastive losses pushing away semantically similar point and image regions and thus disturbing the local semantic structure of the learned representations, and 2) severe class imbalance as pretraining gets dominated by over-represented classes. We propose to alleviate the self-similarity problem through a novel semantically tolerant image-to-point contrastive loss that takes into consideration the semantic distance between positive and negative image regions to minimize contrasting semantically similar point and image regions. Additionally, we address class imbalance by designing a class-agnostic balanced loss that approximates the degree of class imbalance through an aggregate sample-to-samples semantic similarity measure. We demonstrate that our semantically-tolerant contrastive loss with class balancing improves state-of-the art 2D-to-3D representation learning in all evaluation settings on 3D semantic segmentation. Our method consistently outperforms state-of-the-art 2D-to-3D representation learning frameworks across a wide range of 2D self-supervised pretrained models.	['Steven L. Waslander', 'Liam Paull', 'Ali Harakeh', 'Tianshu Kuai', 'Jordan S. K. Hu', 'Anas Mahmoud']	2023-01-12	null	http://openaccess.thecvf.com//content/CVPR2023/html/Mahmoud_Self-Supervised_Image-to-Point_Distillation_via_Semantically_Tolerant_Contrastive_Loss_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Mahmoud_Self-Supervised_Image-to-Point_Distillation_via_Semantically_Tolerant_Contrastive_Loss_CVPR_2023_paper.pdf	cvpr-2023-1	['semantic-textual-similarity']	['natural-language-processing']	[ 3.22358370e-01 1.96868867e-01 -4.60319638e-01 -7.44793057e-01 -9.28284883e-01 -4.60870206e-01 6.21825695e-01 4.44605857e-01 -3.50199372e-01 1.79092005e-01 -6.59833103e-02 -4.76651974e-02 2.00914480e-02 -8.24820936e-01 -1.07648289e+00 -5.62151313e-01 9.60614905e-02 6.07443154e-01 4.76385355e-01 -4.07455266e-01 3.53861868e-01 7.76220858e-01 -2.08810973e+00 1.35149613e-01 9.34990585e-01 1.49715471e+00 1.74243852e-01 1.95189789e-01 -4.74345744e-01 6.25240505e-01 -6.27560079e-01 -4.06050608e-02 7.98883259e-01 -2.40666136e-01 -5.40711284e-01 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6abecf8a-0701-4774-9222-cad475d665ac	annotating-arguments-in-a-corpus-of-opinion	null	null	https://aclanthology.org/2022.lrec-1.201	https://aclanthology.org/2022.lrec-1.201.pdf	Annotating Arguments in a Corpus of Opinion Articles	Interest in argument mining has resulted in an increasing number of argument annotated corpora. However, most focus on English texts with explicit argumentative discourse markers, such as persuasive essays or legal documents. Conversely, we report on the first extensive and consolidated Portuguese argument annotation project focused on opinion articles. We briefly describe the annotation guidelines based on a multi-layered process and analyze the manual annotations produced, highlighting the main challenges of this textual genre. We then conduct a comprehensive inter-annotator agreement analysis, including argumentative discourse units, their classes and relations, and resulting graphs. This analysis reveals that each of these aspects tackles very different kinds of challenges. We observe differences in annotator profiles, motivating our aim of producing a non-aggregated corpus containing the insights of every annotator. We note that the interpretation and identification of token-level arguments is challenging; nevertheless, tasks that focus on higher-level components of the argument structure can obtain considerable agreement. We lay down perspectives on corpus usage, exploiting its multi-faceted nature.	['Miguel Won', 'Bruno Martins', 'Paula Carvalho', 'Rui Sousa-Silva', 'Henrique Lopes Cardoso', 'Luís Trigo', 'Gil Rocha']	null	null	null	null	lrec-2022-6	['argument-mining']	['natural-language-processing']	[ 2.88053632e-01 1.05648613e+00 -5.47464132e-01 -1.73154548e-01 -9.33058321e-01 -1.13626444e+00 1.01519430e+00 9.33188140e-01 -4.70727593e-01 1.05933213e+00 9.95396018e-01 -8.49779904e-01 -4.80549634e-01 -4.03589487e-01 -3.19532067e-01 -3.22940379e-01 3.04762930e-01 6.60530031e-01 2.05980256e-01 -4.82660204e-01 6.96437776e-01 -8.00398663e-02 -1.46828115e+00 8.32069635e-01 1.11151862e+00 4.75677639e-01 -1.92279592e-01 5.81334710e-01 -5.42562485e-01 1.32841527e+00 -1.02622509e+00 -1.08220601e+00 -5.22117853e-01 -5.05368412e-01 -1.62754357e+00 -9.76276323e-02 1.91831738e-01 3.74447554e-01 4.86058146e-01 8.08321118e-01 3.52653891e-01 -4.16018546e-01 6.22440398e-01 -9.18392777e-01 -4.78664100e-01 1.36162865e+00 -2.14856714e-01 3.84912372e-01 6.88825548e-01 -4.27581847e-01 1.51779485e+00 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5316eb73-dded-4b14-a2d8-5e0941d3b4a4	augmented-sbert-data-augmentation-method-for	2010.08240	null	https://arxiv.org/abs/2010.08240v2	https://arxiv.org/pdf/2010.08240v2.pdf	Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks	There are two approaches for pairwise sentence scoring: Cross-encoders, which perform full-attention over the input pair, and Bi-encoders, which map each input independently to a dense vector space. While cross-encoders often achieve higher performance, they are too slow for many practical use cases. Bi-encoders, on the other hand, require substantial training data and fine-tuning over the target task to achieve competitive performance. We present a simple yet efficient data augmentation strategy called Augmented SBERT, where we use the cross-encoder to label a larger set of input pairs to augment the training data for the bi-encoder. We show that, in this process, selecting the sentence pairs is non-trivial and crucial for the success of the method. We evaluate our approach on multiple tasks (in-domain) as well as on a domain adaptation task. Augmented SBERT achieves an improvement of up to 6 points for in-domain and of up to 37 points for domain adaptation tasks compared to the original bi-encoder performance.	['Iryna Gurevych', 'Johannes Daxenberger', 'Nils Reimers', 'Nandan Thakur']	2020-10-16	null	https://aclanthology.org/2021.naacl-main.28	https://aclanthology.org/2021.naacl-main.28.pdf	naacl-2021-4	['sentence-pair-modeling']	['natural-language-processing']	[ 3.23412716e-01 -1.61477253e-02 1.97450500e-02 -7.94182479e-01 -1.51466262e+00 -6.27076507e-01 5.72274625e-01 3.15290600e-01 -5.62457502e-01 8.74752641e-01 4.14568245e-01 -2.05646664e-01 3.50582242e-01 -4.74974990e-01 -8.17161322e-01 -3.93180341e-01 1.76277563e-01 7.71440506e-01 2.79057711e-01 -4.16608602e-01 1.24434151e-01 -1.47565290e-01 -1.22788203e+00 5.07033169e-01 8.07918429e-01 8.61929119e-01 2.02630937e-01 8.48010421e-01 -1.56134948e-01 5.08841872e-01 -6.80007219e-01 -8.80576730e-01 2.44349048e-01 -4.52249080e-01 -1.02288568e+00 -1.81268439e-01 5.03516257e-01 -3.89104635e-01 -8.49247873e-02 8.64486694e-01 5.84262550e-01 1.79097146e-01 6.81535840e-01 -1.00193310e+00 -7.67797709e-01 5.71147203e-01 -4.82238621e-01 1.19610593e-01 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3.40864480e-01 6.67602241e-01 1.07904601e+00 4.49069351e-01 4.26760346e-01 6.59095645e-01 -8.10381353e-01 -8.64851177e-01 -1.21170235e+00 -2.11686909e-01 7.18659639e-01 3.30179423e-01 -6.71521723e-01 -1.21764511e-01 -2.67405920e-02]	[10.909427642822266, 8.566184043884277]
27727233-02d6-4da2-8dc4-2df4a97582d7	speech-to-speech-translation-between	1910.00795	null	https://arxiv.org/abs/1910.00795v2	https://arxiv.org/pdf/1910.00795v2.pdf	Speech-to-speech Translation between Untranscribed Unknown Languages	In this paper, we explore a method for training speech-to-speech translation tasks without any transcription or linguistic supervision. Our proposed method consists of two steps: First, we train and generate discrete representation with unsupervised term discovery with a discrete quantized autoencoder. Second, we train a sequence-to-sequence model that directly maps the source language speech to the target language's discrete representation. Our proposed method can directly generate target speech without any auxiliary or pre-training steps with a source or target transcription. To the best of our knowledge, this is the first work that performed pure speech-to-speech translation between untranscribed unknown languages.	['Andros Tjandra', 'Satoshi Nakamura', 'Sakriani Sakti']	2019-10-02	null	null	null	null	['speech-to-speech-translation']	['speech']	[ 6.28447652e-01 4.64888871e-01 -4.45566885e-02 -5.46140373e-01 -1.13403738e+00 -5.94770014e-01 8.04920018e-01 -2.33794093e-01 -9.73783731e-02 1.03188574e+00 1.60685048e-01 -7.71942139e-01 6.31262839e-01 -6.05092764e-01 -8.51241231e-01 -4.89032120e-01 3.48066598e-01 8.76883984e-01 -4.44726795e-02 -4.11455929e-01 -2.24070251e-01 2.43338004e-01 -9.79282498e-01 4.63752240e-01 8.11625302e-01 5.28137028e-01 4.33109403e-01 8.96176457e-01 -2.97138661e-01 7.34044373e-01 -7.86873758e-01 -1.90300763e-01 1.69072136e-01 -1.04358757e+00 -9.57002640e-01 2.99915195e-01 -3.18319388e-02 -3.54066640e-01 -3.56694490e-01 1.08798265e+00 4.99729037e-01 1.60023838e-01 8.20673287e-01 -8.24665129e-01 -1.30104041e+00 8.42297614e-01 7.97581524e-02 1.23720981e-01 4.55236256e-01 4.86501865e-02 7.69479215e-01 -9.91951942e-01 5.89499712e-01 1.14858437e+00 7.66571239e-02 7.40307033e-01 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c673fc17-7df5-4fc7-a520-c63126bce827	densepose-from-wifi	2301.00250	null	https://arxiv.org/abs/2301.00250v1	https://arxiv.org/pdf/2301.00250v1.pdf	DensePose From WiFi	Advances in computer vision and machine learning techniques have led to significant development in 2D and 3D human pose estimation from RGB cameras, LiDAR, and radars. However, human pose estimation from images is adversely affected by occlusion and lighting, which are common in many scenarios of interest. Radar and LiDAR technologies, on the other hand, need specialized hardware that is expensive and power-intensive. Furthermore, placing these sensors in non-public areas raises significant privacy concerns. To address these limitations, recent research has explored the use of WiFi antennas (1D sensors) for body segmentation and key-point body detection. This paper further expands on the use of the WiFi signal in combination with deep learning architectures, commonly used in computer vision, to estimate dense human pose correspondence. We developed a deep neural network that maps the phase and amplitude of WiFi signals to UV coordinates within 24 human regions. The results of the study reveal that our model can estimate the dense pose of multiple subjects, with comparable performance to image-based approaches, by utilizing WiFi signals as the only input. This paves the way for low-cost, broadly accessible, and privacy-preserving algorithms for human sensing.	['Fernando de la Torre', 'Dong Huang', 'Jiaqi Geng']	2022-12-31	null	null	null	null	['body-detection', '3d-human-pose-estimation']	['computer-vision', 'computer-vision']	[ 3.97447735e-01 -2.03006759e-01 1.68411553e-01 -4.98514533e-01 -6.13836527e-01 -4.89054590e-01 1.77251045e-02 6.95942715e-02 -6.06510997e-01 5.16574442e-01 -8.68809298e-02 2.55545199e-01 1.54140756e-01 -9.39938664e-01 -7.54246116e-01 -5.51579595e-01 -1.77946478e-01 3.32461685e-01 -1.83841735e-01 1.78755671e-01 -3.21218997e-01 5.52910864e-01 -1.52563655e+00 -2.05553845e-01 4.60313648e-01 1.43498766e+00 -5.07473409e-01 4.43472326e-01 5.64698696e-01 -2.95213878e-01 -5.85562646e-01 -5.55666268e-01 5.80250204e-01 -3.13494802e-02 -1.24406375e-01 -1.31255940e-01 9.41255629e-01 -6.35056019e-01 -3.56857270e-01 7.55904019e-01 9.23689842e-01 -1.00703195e-01 2.44941488e-01 -1.33400202e+00 -1.12608328e-01 -5.63689172e-02 -8.15344632e-01 -2.91386396e-01 8.93739820e-01 -2.48653162e-02 3.74285579e-01 -4.89338905e-01 1.81372553e-01 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d7a4ed53-b447-47dc-be26-3d709a3c1d07	explaining-classes-through-stable-word	null	null	https://aclanthology.org/2022.findings-acl.85	https://aclanthology.org/2022.findings-acl.85.pdf	Explaining Classes through Stable Word Attributions	Input saliency methods have recently become a popular tool for explaining predictions of deep learning models in NLP. Nevertheless, there has been little work investigating methods for aggregating prediction-level explanations to the class level, nor has a framework for evaluating such class explanations been established. We explore explanations based on XLM-R and the Integrated Gradients input attribution method, and propose 1) the Stable Attribution Class Explanation method (SACX) to extract keyword lists of classes in text classification tasks, and 2) a framework for the systematic evaluation of the keyword lists. We find that explanations of individual predictions are prone to noise, but that stable explanations can be effectively identified through repeated training and explanation. We evaluate on web register data and show that the class explanations are linguistically meaningful and distinguishing of the classes.	['Veronika Laippala', 'Filip Ginter', 'Amanda Myntti', 'Aki-Juhani Kyröläinen', 'Samuel Rönnqvist']	null	null	null	null	findings-acl-2022-5	['xlm-r']	['natural-language-processing']	[ 5.81475794e-01 8.86752844e-01 -6.31524980e-01 -7.84422755e-01 -7.86655843e-01 -4.98439193e-01 8.87410223e-01 4.54477221e-01 -9.25111026e-03 7.57515073e-01 7.12251842e-01 -7.37816393e-01 -6.29523516e-01 -3.34860504e-01 -6.54321194e-01 -2.48069808e-01 3.41861695e-01 6.48513377e-01 8.09799284e-02 -1.33234203e-01 7.45644510e-01 3.47611904e-01 -1.84089172e+00 9.02368426e-01 1.09656835e+00 7.28459895e-01 1.83670089e-01 5.46811283e-01 -6.14241421e-01 9.28281784e-01 -5.63127875e-01 -6.75489724e-01 -2.50692904e-01 -7.02676296e-01 -1.17599213e+00 -3.02543730e-01 5.66695988e-01 -1.22139722e-01 1.91616461e-01 8.87536407e-01 1.94987610e-01 8.38882327e-02 1.05724847e+00 -1.44755888e+00 -1.16833639e+00 1.31221676e+00 -4.39817496e-02 2.69980371e-01 3.88890624e-01 -2.52107412e-01 1.55274761e+00 -1.23048854e+00 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9e5a6336-37d0-485f-8957-a171ee441342	fots-fast-oriented-text-spotting-with-a	1801.01671	null	http://arxiv.org/abs/1801.01671v2	http://arxiv.org/pdf/1801.01671v2.pdf	FOTS: Fast Oriented Text Spotting with a Unified Network	Incidental scene text spotting is considered one of the most difficult and valuable challenges in the document analysis community. Most existing methods treat text detection and recognition as separate tasks. In this work, we propose a unified end-to-end trainable Fast Oriented Text Spotting (FOTS) network for simultaneous detection and recognition, sharing computation and visual information among the two complementary tasks. Specially, RoIRotate is introduced to share convolutional features between detection and recognition. Benefiting from convolution sharing strategy, our FOTS has little computation overhead compared to baseline text detection network, and the joint training method learns more generic features to make our method perform better than these two-stage methods. Experiments on ICDAR 2015, ICDAR 2017 MLT, and ICDAR 2013 datasets demonstrate that the proposed method outperforms state-of-the-art methods significantly, which further allows us to develop the first real-time oriented text spotting system which surpasses all previous state-of-the-art results by more than 5% on ICDAR 2015 text spotting task while keeping 22.6 fps.	['Yu Qiao', 'Shi Yan', 'Ding Liang', 'Xuebo Liu', 'Junjie Yan', 'Dagui Chen']	2018-01-05	fots-fast-oriented-text-spotting-with-a-1	http://openaccess.thecvf.com/content_cvpr_2018/html/Liu_FOTS_Fast_Oriented_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Liu_FOTS_Fast_Oriented_CVPR_2018_paper.pdf	cvpr-2018-6	['text-spotting']	['computer-vision']	[ 2.98514992e-01 -3.38148355e-01 1.23738898e-02 -2.36720949e-01 -7.30059624e-01 -3.74416888e-01 7.46028960e-01 3.63887288e-03 -6.12873435e-01 3.04770544e-02 2.16305275e-02 -2.76054859e-01 2.69450605e-01 -4.63614464e-01 -5.62727511e-01 -5.38032472e-01 4.17511135e-01 5.80642760e-01 3.68348658e-01 7.56242648e-02 2.59543300e-01 2.42500171e-01 -1.35581160e+00 6.10778809e-01 7.75477886e-01 1.08563387e+00 5.96312940e-01 7.98555791e-01 -4.25945193e-01 7.68853009e-01 -4.76551831e-01 -4.75663930e-01 1.31447464e-01 2.75535975e-02 -5.15650868e-01 2.08162352e-01 1.02637374e+00 -6.71239555e-01 -8.26105237e-01 8.29217136e-01 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f4bcc869-96c8-4ce5-9035-9b5a776830e5	test-time-style-shifting-handling-arbitrary	2306.04911	null	https://arxiv.org/abs/2306.04911v2	https://arxiv.org/pdf/2306.04911v2.pdf	Test-Time Style Shifting: Handling Arbitrary Styles in Domain Generalization	In domain generalization (DG), the target domain is unknown when the model is being trained, and the trained model should successfully work on an arbitrary (and possibly unseen) target domain during inference. This is a difficult problem, and despite active studies in recent years, it remains a great challenge. In this paper, we take a simple yet effective approach to tackle this issue. We propose test-time style shifting, which shifts the style of the test sample (that has a large style gap with the source domains) to the nearest source domain that the model is already familiar with, before making the prediction. This strategy enables the model to handle any target domains with arbitrary style statistics, without additional model update at test-time. Additionally, we propose style balancing, which provides a great platform for maximizing the advantage of test-time style shifting by handling the DG-specific imbalance issues. The proposed ideas are easy to implement and successfully work in conjunction with various other DG schemes. Experimental results on different datasets show the effectiveness of our methods.	['Jaekyun Moon', 'Soyeong Kim', 'Dong-Jun Han', 'Jungwuk Park']	2023-06-08	null	null	null	null	['domain-generalization']	['methodology']	[ 2.77258337e-01 -1.92746118e-01 -3.23702514e-01 -5.85024536e-01 -5.41739702e-01 -7.13180482e-01 3.51578414e-01 -1.48977175e-01 -6.49062991e-02 1.15585792e+00 -2.59952933e-01 -9.43528190e-02 -2.16165736e-01 -9.34465647e-01 -6.67962670e-01 -8.28982711e-01 3.19345564e-01 9.36974585e-01 4.72617656e-01 -1.20026879e-01 3.26152086e-01 2.82178730e-01 -1.48171127e+00 1.40678078e-01 1.36502600e+00 9.85313892e-01 4.17883545e-01 2.01038226e-01 -2.26933405e-01 6.76308692e-01 -9.66401815e-01 -5.11109948e-01 1.62610620e-01 -4.94795799e-01 -7.43613720e-01 1.37811020e-01 3.25423479e-01 -2.98545867e-01 5.09690829e-02 1.08780694e+00 6.50519252e-01 9.09581408e-02 6.40890777e-01 -1.36922359e+00 -5.20134747e-01 4.10035849e-01 -4.24914777e-01 1.47403568e-01 3.95651720e-02 -9.56504494e-02 6.04878366e-01 -8.21588397e-01 5.74320912e-01 1.18211937e+00 4.80867803e-01 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e30f62c4-f011-4e6f-a91b-8fd40001209f	materials-property-prediction-with	2211.02235	null	https://arxiv.org/abs/2211.02235v2	https://arxiv.org/pdf/2211.02235v2.pdf	Materials Property Prediction with Uncertainty Quantification: A Benchmark Study	Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models. It can also be used in active learning to train better models by focusing on getting new training data from uncertain regions. There are several categories of UQ methods each considering different types of uncertainty sources. Here we conduct a comprehensive evaluation on the UQ methods for graph neural network based materials property prediction and evaluate how they truly reflect the uncertainty that we want in error bound estimation or active learning. Our experimental results over four crystal materials datasets (including formation energy, adsorption energy, total energy, and band gap properties) show that the popular ensemble methods for uncertainty estimation is NOT the best choice for UQ in materials property prediction. For the convenience of the community, all the source code and data sets can be accessed freely at \url{https://github.com/usccolumbia/materialsUQ}.	['Jianjun Hu', 'Sadman Sadeed Omee', 'Rongzhi Dong', 'Daniel Varivoda']	2022-11-04	null	null	null	null	['formation-energy', 'total-energy']	['miscellaneous', 'miscellaneous']	[ 8.97963867e-02 1.30978882e-01 -4.62065727e-01 -4.30081666e-01 -1.11583638e+00 -2.50075191e-01 2.21249253e-01 6.19737685e-01 6.78997859e-02 1.45734537e+00 -2.05518231e-02 -2.20588505e-01 -4.47798759e-01 -1.28131831e+00 -9.86585379e-01 -1.09141755e+00 -8.21270496e-02 7.27109671e-01 2.49425232e-01 4.71591391e-02 4.59361136e-01 3.54042977e-01 -1.40632749e+00 -5.41176386e-02 1.41667306e+00 1.42484927e+00 4.11886461e-02 3.49665545e-02 -8.24383944e-02 6.14031553e-01 -9.11433026e-02 -1.82705626e-01 -7.93374926e-02 5.77052906e-02 -9.17214274e-01 -9.34972286e-01 -1.55091122e-01 5.62095046e-02 -9.73670650e-03 1.12860918e+00 6.39667392e-01 4.02126759e-01 1.18962157e+00 -1.07921505e+00 -6.67999685e-01 1.18920290e+00 -6.30043447e-02 -2.01424137e-02 4.64478910e-01 -1.38128653e-01 1.06690526e+00 -8.62656176e-01 4.46359992e-01 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55ff4b4b-95e6-46ac-9f8a-77c4fe256afc	minimum-barrier-salient-object-detection-at	null	null	http://openaccess.thecvf.com/content_iccv_2015/html/Zhang_Minimum_Barrier_Salient_ICCV_2015_paper.html	http://openaccess.thecvf.com/content_iccv_2015/papers/Zhang_Minimum_Barrier_Salient_ICCV_2015_paper.pdf	Minimum Barrier Salient Object Detection at 80 FPS	We propose a highly efficient, yet powerful, salient object detection method based on the Minimum Barrier Distance (MBD) Transform. The MBD transform is robust to pixel-value fluctuation, and thus can be effectively applied on raw pixels without region abstraction. We present an approximate MBD transform algorithm with 100X speedup over the exact algorithm. An error bound analysis is also provided. Powered by this fast MBD transform algorithm, the proposed salient object detection method runs at 80 FPS, and significantly outperforms previous methods with similar speed on four large benchmark datasets, and achieves comparable or better performance than state-of-the-art methods. Furthermore, a technique based on color whitening is proposed to extend our method to leverage the appearance-based backgroundness cue. This extended version further improves the performance, while still being one order of magnitude faster than all the other leading methods.	['Radomir Mech', 'Brian Price', 'Xiaohui Shen', 'Zhe Lin', 'Stan Sclaroff', 'Jianming Zhang']	2015-12-01	null	null	null	iccv-2015-12	['video-salient-object-detection']	['computer-vision']	[ 4.35142547e-01 -1.57782212e-01 -2.22284704e-01 1.67217806e-01 -8.51894975e-01 -3.92942846e-01 4.32754338e-01 3.04339021e-01 -4.36075628e-01 4.85366702e-01 -5.71700782e-02 -2.63942599e-01 5.01107275e-01 -6.08646572e-01 -7.25572228e-01 -7.83160985e-01 -8.30742717e-02 -2.32422575e-01 1.28047693e+00 -1.73302412e-01 4.78574127e-01 5.29758275e-01 -1.74822164e+00 8.67710039e-02 7.31322348e-01 1.38251698e+00 4.70919073e-01 7.01084077e-01 1.32123068e-01 7.73334801e-01 -4.02140230e-01 -5.99841326e-02 5.68944037e-01 -1.60675004e-01 -6.50955796e-01 2.60246117e-02 8.33091497e-01 -7.33406723e-01 -2.31958419e-01 1.07794952e+00 5.54483891e-01 2.44621664e-01 2.27897376e-01 -9.26233411e-01 -6.13260210e-01 2.71601290e-01 -1.23859465e+00 4.98029232e-01 2.05576956e-01 -9.96613875e-02 9.17549372e-01 -1.12848651e+00 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d81996a5-1d0e-4073-bb56-bf791423d742	learning-to-reason-deductively-math-word	2203.10316	null	https://arxiv.org/abs/2203.10316v4	https://arxiv.org/pdf/2203.10316v4.pdf	Learning to Reason Deductively: Math Word Problem Solving as Complex Relation Extraction	Solving math word problems requires deductive reasoning over the quantities in the text. Various recent research efforts mostly relied on sequence-to-sequence or sequence-to-tree models to generate mathematical expressions without explicitly performing relational reasoning between quantities in the given context. While empirically effective, such approaches typically do not provide explanations for the generated expressions. In this work, we view the task as a complex relation extraction problem, proposing a novel approach that presents explainable deductive reasoning steps to iteratively construct target expressions, where each step involves a primitive operation over two quantities defining their relation. Through extensive experiments on four benchmark datasets, we show that the proposed model significantly outperforms existing strong baselines. We further demonstrate that the deductive procedure not only presents more explainable steps but also enables us to make more accurate predictions on questions that require more complex reasoning.	['Wei Lu', 'Jierui Li', 'Zhanming Jie']	2022-03-19	null	https://aclanthology.org/2022.acl-long.410	https://aclanthology.org/2022.acl-long.410.pdf	acl-2022-5	['math-word-problem-solving', 'relational-reasoning', 'math-word-problem-solving', 'math-word-problem-solving']	['knowledge-base', 'natural-language-processing', 'reasoning', 'time-series']	[ 4.61259753e-01 4.11364913e-01 -1.75750762e-01 -6.26674294e-01 -8.96947384e-01 -8.78760755e-01 8.24028909e-01 4.22534943e-01 -2.09126621e-02 8.59587908e-01 2.82374769e-01 -1.05601692e+00 -1.53871800e-03 -1.38557673e+00 -8.71163428e-01 -1.11363130e-02 2.58760631e-01 5.46044648e-01 -3.21362205e-02 -4.54669148e-01 4.14654106e-01 4.06357259e-01 -1.21778524e+00 5.15169859e-01 1.19817030e+00 7.23016918e-01 -3.11871439e-01 6.40173078e-01 -7.89535880e-01 1.59666502e+00 -6.39263988e-01 -1.15482306e+00 1.74095109e-01 -6.13042831e-01 -1.20499420e+00 -1.92284063e-01 5.38228512e-01 -3.82370532e-01 4.15181555e-03 1.09341347e+00 -2.16614857e-01 2.85323650e-01 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53ebc0c5-e1fd-4d0c-b429-cb4e9555cc0a	latent-space-unsupervised-semantic	2207.11067	null	https://arxiv.org/abs/2207.11067v2	https://arxiv.org/pdf/2207.11067v2.pdf	Latent Space Unsupervised Semantic Segmentation	The development of compact and energy-efficient wearable sensors has led to an increase in the availability of biosignals. To analyze these continuously recorded, and often multidimensional, time series at scale, being able to conduct meaningful unsupervised data segmentation is an auspicious target. A common way to achieve this is to identify change-points within the time series as the segmentation basis. However, traditional change-point detection algorithms often come with drawbacks, limiting their real-world applicability. Notably, they generally rely on the complete time series to be available and thus cannot be used for real-time applications. Another common limitation is that they poorly (or cannot) handle the segmentation of multidimensional time series. Consequently, the main contribution of this work is to propose a novel unsupervised segmentation algorithm for multidimensional time series named Latent Space Unsupervised Semantic Segmentation (LS-USS), which was designed to work easily with both online and batch data. When comparing LS-USS against other state-of-the-art change-point detection algorithms on a variety of real-world datasets, in both the offline and real-time setting, LS-USS systematically achieves on par or better performances.	['Ulysse Côté-Allard', 'Jim Tørresen', 'Knut J. Strømmen']	2022-07-22	null	null	null	null	['unsupervised-semantic-segmentation']	['computer-vision']	[ 3.76215219e-01 -5.13926685e-01 -1.62652448e-01 -2.09785044e-01 -4.89126295e-01 -6.65380120e-01 2.84326613e-01 6.77890837e-01 -3.53967428e-01 4.30420399e-01 -2.90070832e-01 -7.12483525e-02 -2.97674835e-01 -6.52105153e-01 -2.79236853e-01 -6.63847148e-01 -1.04775995e-01 2.40646422e-01 2.58155435e-01 1.10412620e-01 9.67172608e-02 4.93213564e-01 -1.77084649e+00 -2.49122471e-01 1.04562616e+00 1.06482911e+00 1.27588227e-01 2.52176017e-01 -2.05203041e-01 -1.95152283e-01 -4.11983341e-01 3.11469764e-01 1.73983231e-01 -6.44404650e-01 -4.03121501e-01 1.14503220e-01 -2.49064267e-01 1.07524604e-01 2.31402725e-01 1.02352118e+00 5.65125704e-01 3.10745388e-01 4.27907139e-01 -1.20946860e+00 2.13668406e-01 2.23634556e-01 -3.14806491e-01 3.82246464e-01 3.06452870e-01 -2.43917152e-01 6.45415843e-01 -5.47259390e-01 6.78873360e-01 7.34729052e-01 6.84621155e-01 3.65605235e-01 -1.62371349e+00 -3.22221518e-01 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b1b5c56c-2e35-455a-ba6a-e784f9a878d3	segment-anything-meets-semantic-communication	2306.02094	null	https://arxiv.org/abs/2306.02094v1	https://arxiv.org/pdf/2306.02094v1.pdf	Segment Anything Meets Semantic Communication	In light of the diminishing returns of traditional methods for enhancing transmission rates, the domain of semantic communication presents promising new frontiers. Focusing on image transmission, this paper explores the application of foundation models, particularly the Segment Anything Model (SAM) developed by Meta AI Research, to improve semantic communication. SAM is a promptable image segmentation model that has gained attention for its ability to perform zero-shot segmentation tasks without explicit training or domain-specific knowledge. By employing SAM's segmentation capability and lightweight neural network architecture for semantic coding, we propose a practical approach to semantic communication. We demonstrate that this approach retains critical semantic features, achieving higher image reconstruction quality and reducing communication overhead. This practical solution eliminates the resource-intensive stage of training a segmentation model and can be applied to any semantic coding architecture, paving the way for real-world applications.	['Hyundong Shin', 'Chaoning Zhang', 'Brian Estadimas Arfeto', 'Shehbaz Tariq']	2023-06-03	null	null	null	null	['zero-shot-segmentation', 'image-reconstruction']	['computer-vision', 'computer-vision']	[ 9.62071896e-01 6.98789179e-01 -4.82801616e-01 -3.31304371e-01 -3.27378780e-01 -9.50172693e-02 4.96774435e-01 -5.33881225e-02 -4.50672805e-01 4.73983139e-01 2.27811322e-01 -4.35713291e-01 -1.59169137e-01 -1.02251720e+00 -4.30867136e-01 -2.31917337e-01 2.08916832e-02 3.34216088e-01 3.06340516e-01 -2.01159552e-01 4.05368060e-01 3.01973075e-01 -1.50725484e+00 3.28174025e-01 7.95343697e-01 1.31135035e+00 8.24264228e-01 6.36765182e-01 -6.79600775e-01 1.01136827e+00 -7.42560744e-01 -3.22608024e-01 -1.02294333e-01 -6.20867550e-01 -1.39063227e+00 2.64755756e-01 -2.95082659e-01 -3.75258148e-01 -4.15471286e-01 1.12112880e+00 2.04728663e-01 -1.45357713e-01 3.70798498e-01 -1.05005085e+00 -7.00748086e-01 9.60816860e-01 -1.40277192e-01 -2.65561678e-02 4.88614589e-01 -3.76513243e-01 8.38500798e-01 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94d90c1d-8b91-4925-88b4-cd94bd380782	ensemble-learning-of-myocardial-displacements	2303.06744	null	https://arxiv.org/abs/2303.06744v1	https://arxiv.org/pdf/2303.06744v1.pdf	Ensemble Learning of Myocardial Displacements for Myocardial Infarction Detection in Echocardiography	Early detection and localization of myocardial infarction (MI) can reduce the severity of cardiac damage through timely treatment interventions. In recent years, deep learning techniques have shown promise for detecting MI in echocardiographic images. However, there has been no examination of how segmentation accuracy affects MI classification performance and the potential benefits of using ensemble learning approaches. Our study investigates this relationship and introduces a robust method that combines features from multiple segmentation models to improve MI classification performance by leveraging ensemble learning. Our method combines myocardial segment displacement features from multiple segmentation models, which are then input into a typical classifier to estimate the risk of MI. We validated the proposed approach on two datasets: the public HMC-QU dataset (109 echocardiograms) for training and validation, and an E-Hospital dataset (60 echocardiograms) from a local clinical site in Vietnam for independent testing. Model performance was evaluated based on accuracy, sensitivity, and specificity. The proposed approach demonstrated excellent performance in detecting MI. The results showed that the proposed approach outperformed the state-of-the-art feature-based method. Further research is necessary to determine its potential use in clinical settings as a tool to assist cardiologists and technicians with objective assessments and reduce dependence on operator subjectivity. Our research codes are available on GitHub at https://github.com/vinuni-vishc/mi-detection-echo.	['Hieu Pham', 'Long Tran', 'Thuy Nguyen', 'Vinh Le', 'Phuong Tran', 'Bach Do', 'Hanh Van', 'Thanh Le', 'Quang Nguyen', 'Hung Pham', 'Dai Tran', 'Phi Nguyen', 'Nguyen Tuan']	2023-03-12	null	null	null	null	['myocardial-infarction-detection']	['medical']	[ 1.79557726e-01 -4.29062545e-01 -1.89608693e-01 -2.74275869e-01 -1.13200057e+00 -4.81943488e-01 -1.56614661e-01 2.68752664e-01 -3.92060906e-01 4.38458651e-01 8.96042399e-03 -7.62179017e-01 -3.94235641e-01 -6.25409544e-01 -2.39850029e-01 -7.23893046e-01 -4.66563553e-01 4.06277269e-01 1.16742834e-01 4.13067907e-01 3.69922251e-01 5.12801230e-01 -9.58741009e-01 4.66716349e-01 9.26595569e-01 7.77261138e-01 1.88163206e-01 1.06333780e+00 4.51568663e-01 6.73018336e-01 -5.36458910e-01 7.13627636e-02 3.41456383e-01 -8.70929241e-01 -4.94834721e-01 5.81124797e-02 2.56679267e-01 -4.52622473e-01 -1.28517181e-01 1.69258356e-01 1.17865443e+00 -2.64514983e-01 5.91996074e-01 -6.63570523e-01 -1.46503434e-01 5.49219251e-01 -2.00286344e-01 9.28374350e-01 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4f20fd80-48ef-4b09-a1a0-4c56203834cf	data-augmentation-for-low-resource-named	2108.11703	null	https://arxiv.org/abs/2108.11703v1	https://arxiv.org/pdf/2108.11703v1.pdf	Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation	The state of art natural language processing systems relies on sizable training datasets to achieve high performance. Lack of such datasets in the specialized low resource domains lead to suboptimal performance. In this work, we adapt backtranslation to generate high quality and linguistically diverse synthetic data for low-resource named entity recognition. We perform experiments on two datasets from the materials science (MaSciP) and biomedical domains (S800). The empirical results demonstrate the effectiveness of our proposed augmentation strategy, particularly in the low-resource scenario.	['Stefan Langer', 'Usama Yaseen']	2021-08-26	null	https://aclanthology.org/2021.icon-main.43	https://aclanthology.org/2021.icon-main.43.pdf	icon-2021-12	['low-resource-named-entity-recognition']	['natural-language-processing']	[ 3.12702745e-01 -7.54690543e-02 -5.75887784e-02 -4.80514586e-01 -1.02133512e+00 -5.79059362e-01 6.33316815e-01 1.90660760e-01 -9.34791148e-01 1.43008375e+00 2.82290429e-01 -2.63116330e-01 4.13580805e-01 -6.32633269e-01 -6.31942868e-01 -2.00223982e-01 3.58520389e-01 6.56400859e-01 5.96888438e-02 -3.41040343e-01 1.28220975e-01 5.00075698e-01 -1.12171543e+00 5.60268700e-01 1.18292868e+00 3.51766616e-01 1.81418046e-01 4.35585827e-01 -5.99679291e-01 4.32432860e-01 -8.33672345e-01 -6.52603269e-01 4.17981058e-01 -4.54015493e-01 -9.25138950e-01 -2.00822040e-01 -1.13555118e-01 2.70607799e-01 1.95367962e-01 9.62559819e-01 8.31561565e-01 1.45232469e-01 4.92903084e-01 -9.79612410e-01 -7.22263277e-01 7.59644330e-01 -2.57117033e-01 3.19545031e-01 5.44085383e-01 2.70601422e-01 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889945a4-9a91-4d76-9a06-e4d3c315bb29	word2box-capturing-set-theoretic-semantics-of	null	null	https://openreview.net/forum?id=NThz_6MqDuG	https://openreview.net/pdf?id=NThz_6MqDuG	Word2Box: Capturing Set-Theoretic Semantics of Words using BoxEmbeddings	Learning representations of words in a continuous space is perhaps the most fundamental task in NLP, a prerequisite for nearly all modern machine-learning techniques. Often the objective is to capture distributional similarity via vector dot product, however this is just one relation between word meanings we may wish to capture. It is natural to consider words as (soft) equivalence classes based on similarity, it is natural to expect the ability to perform set-theoretic operations (intersection, union, difference) on these representations. This is particularly relevant for words which are homographs- for example, “tongue”∩“body” should be similar to “mouth”, while “tongue”∩“language” should be similar to “dialect”. Box embeddings are a novel region-based representation which provide the capability to perform these set-theoretic operations. In this work, we provide a fuzzy-set interpretation of box embeddings, and train box embeddings with a CBOW objective where contexts are represented using intersection. We demonstrate improved performance on various word similarity tasks, particularly on less common words, and perform a quantitative and qualitative analysis exploring the additional unique expressivity provided by Word2Box.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['word-similarity']	['natural-language-processing']	[ 0.01374361 0.01143906 -0.35563344 -0.6269907 -0.36879414 -0.86890584 0.9689053 0.9416275 -0.52065617 0.21706575 0.5572718 -0.43636215 -0.3900696 -1.0151875 -0.31398875 -0.5263633 -0.28508645 0.4612023 -0.16546446 -0.72714186 0.2644587 0.67067295 -1.5689656 0.05955157 0.75490546 0.9156713 0.05133103 0.16529647 -0.5410728 0.01369202 -0.43591446 -0.52897704 0.10706875 -0.06465216 -0.88829225 -0.14156523 0.46961954 0.29863906 -0.03959217 1.4080763 0.16620871 0.59544045 1.2372016 -1.0675148 -1.1851473 0.52806455 -0.30993482 0.18529513 0.6688535 -0.2741551 1.6020786 -0.965299 0.43820232 1.567577 0.5403269 0.44217223 -1.2894391 -0.13190769 0.10635003 0.09711137 -1.420355 0.06911185 0.5689488 -0.35816315 0.79494077 0.46034485 0.43910098 0.82774156 0.09007438 0.50355613 0.98666704 -0.7622711 0.32699907 0.4820761 0.5330994 0.46699053 0.3895315 -0.08553975 -0.0714257 -0.06479191 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954bb935-c091-4daf-838b-5c3cd917843b	crafting-a-multi-task-cnn-for-viewpoint	1609.03894	null	http://arxiv.org/abs/1609.03894v1	http://arxiv.org/pdf/1609.03894v1.pdf	Crafting a multi-task CNN for viewpoint estimation	Convolutional Neural Networks (CNNs) were recently shown to provide state-of-the-art results for object category viewpoint estimation. However different ways of formulating this problem have been proposed and the competing approaches have been explored with very different design choices. This paper presents a comparison of these approaches in a unified setting as well as a detailed analysis of the key factors that impact performance. Followingly, we present a new joint training method with the detection task and demonstrate its benefit. We also highlight the superiority of classification approaches over regression approaches, quantify the benefits of deeper architectures and extended training data, and demonstrate that synthetic data is beneficial even when using ImageNet training data. By combining all these elements, we demonstrate an improvement of approximately 5% mAVP over previous state-of-the-art results on the Pascal3D+ dataset. In particular for their most challenging 24 view classification task we improve the results from 31.1% to 36.1% mAVP.	['Renaud Marlet', 'Mathieu Aubry', 'Francisco Massa']	2016-09-13	null	null	null	null	['viewpoint-estimation']	['computer-vision']	[ 1.09920837e-01 -3.48844901e-02 2.79974677e-02 -5.07318974e-01 -7.57914305e-01 -4.39337015e-01 9.14955437e-01 -2.74963737e-01 -5.66579163e-01 3.82922590e-01 -1.93332396e-02 -8.17249864e-02 5.68207689e-02 -4.41916645e-01 -8.20747554e-01 -5.65835416e-01 4.36984226e-02 3.00946236e-01 3.28494102e-01 -4.49227467e-02 2.46684968e-01 7.24202275e-01 -1.89952624e+00 4.27420765e-01 3.21209580e-01 1.23553050e+00 -2.23431853e-03 6.94467485e-01 1.94917858e-01 6.66194081e-01 -7.47915685e-01 -4.03879702e-01 4.11919057e-01 9.85254943e-02 -6.46654487e-01 1.26043409e-01 1.10210860e+00 -5.72445631e-01 -2.97618061e-01 6.03489339e-01 7.14349210e-01 -1.33364648e-01 7.61214495e-01 -1.18218565e+00 -4.39431757e-01 1.52838930e-01 -6.61494017e-01 2.04276249e-01 2.99503207e-02 8.85273442e-02 8.51630211e-01 -1.06130731e+00 6.32411361e-01 1.17522395e+00 8.55509937e-01 4.70058501e-01 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7318469a-e5ec-4374-88cd-b4b4e34b6ee9	escoxlm-r-multilingual-taxonomy-driven-pre	2305.12092	null	https://arxiv.org/abs/2305.12092v1	https://arxiv.org/pdf/2305.12092v1.pdf	ESCOXLM-R: Multilingual Taxonomy-driven Pre-training for the Job Market Domain	The increasing number of benchmarks for Natural Language Processing (NLP) tasks in the computational job market domain highlights the demand for methods that can handle job-related tasks such as skill extraction, skill classification, job title classification, and de-identification. While some approaches have been developed that are specific to the job market domain, there is a lack of generalized, multilingual models and benchmarks for these tasks. In this study, we introduce a language model called ESCOXLM-R, based on XLM-R, which uses domain-adaptive pre-training on the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy, covering 27 languages. The pre-training objectives for ESCOXLM-R include dynamic masked language modeling and a novel additional objective for inducing multilingual taxonomical ESCO relations. We comprehensively evaluate the performance of ESCOXLM-R on 6 sequence labeling and 3 classification tasks in 4 languages and find that it achieves state-of-the-art results on 6 out of 9 datasets. Our analysis reveals that ESCOXLM-R performs better on short spans and outperforms XLM-R on entity-level and surface-level span-F1, likely due to ESCO containing short skill and occupation titles, and encoding information on the entity-level.	['Barbara Plank', 'Rob van der Goot', 'Mike Zhang']	2023-05-20	null	null	null	null	['de-identification', 'xlm-r']	['natural-language-processing', 'natural-language-processing']	[ 1.99996665e-01 1.94354206e-02 -7.07388878e-01 -4.06100661e-01 -8.32207263e-01 -6.61774278e-01 6.22070789e-01 3.56626242e-01 -8.98338318e-01 9.30335939e-01 4.90964472e-01 -4.79099929e-01 -5.76301396e-01 -4.19848025e-01 -4.86188710e-01 -3.62285338e-02 2.72211105e-01 1.13188672e+00 -7.17445314e-02 -5.84190190e-01 -9.66160297e-02 4.38795745e-01 -1.44372559e+00 3.68804067e-01 8.91747653e-01 6.62868619e-01 3.56403381e-01 5.29769897e-01 -2.94905216e-01 1.10535383e+00 -5.19821763e-01 -9.01100159e-01 5.87174222e-02 -3.64163965e-01 -1.46139038e+00 -2.05826640e-01 5.48463881e-01 5.98517537e-01 -2.12510139e-01 9.49593723e-01 5.13717711e-01 2.38345727e-01 7.58868158e-01 -1.07256317e+00 -6.84750140e-01 8.46836865e-01 -3.67938131e-01 2.22481042e-01 5.67208767e-01 -1.00533530e-01 1.10439599e+00 -6.95054233e-01 1.21626222e+00 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254bff46-539d-45a0-b08c-cd702c62f5d6	memefier-dual-stage-modality-fusion-for-image	2304.02906	null	https://arxiv.org/abs/2304.02906v2	https://arxiv.org/pdf/2304.02906v2.pdf	MemeFier: Dual-stage Modality Fusion for Image Meme Classification	Hate speech is a societal problem that has significantly grown through the Internet. New forms of digital content such as image memes have given rise to spread of hate using multimodal means, being far more difficult to analyse and detect compared to the unimodal case. Accurate automatic processing, analysis and understanding of this kind of content will facilitate the endeavor of hindering hate speech proliferation through the digital world. To this end, we propose MemeFier, a deep learning-based architecture for fine-grained classification of Internet image memes, utilizing a dual-stage modality fusion module. The first fusion stage produces feature vectors containing modality alignment information that captures non-trivial connections between the text and image of a meme. The second fusion stage leverages the power of a Transformer encoder to learn inter-modality correlations at the token level and yield an informative representation. Additionally, we consider external knowledge as an additional input, and background image caption supervision as a regularizing component. Extensive experiments on three widely adopted benchmarks, i.e., Facebook Hateful Memes, Memotion7k and MultiOFF, indicate that our approach competes and in some cases surpasses state-of-the-art. Our code is available on https://github.com/ckoutlis/memefier.	['Symeon Papadopoulos', 'Manos Schinas', 'Christos Koutlis']	2023-04-06	null	null	null	null	['meme-classification']	['natural-language-processing']	[ 1.96595654e-01 -1.76390454e-01 -5.33503741e-02 6.43603057e-02 -5.52904010e-01 -7.16787398e-01 1.17933714e+00 1.76744759e-01 -4.26594734e-01 5.71376681e-01 5.29732227e-01 -4.95998524e-02 3.46968770e-01 -4.81214792e-01 -6.15042567e-01 -6.19515657e-01 4.12868291e-01 -2.10837345e-03 -3.51193473e-02 -4.25803453e-01 4.15208608e-01 -9.28307418e-03 -1.25772202e+00 5.71017265e-01 6.51929617e-01 1.01989675e+00 -2.44429216e-01 5.52102685e-01 -2.83840686e-01 1.34974074e+00 -4.57039624e-01 -1.05196667e+00 -5.94345741e-02 -2.85299361e-01 -8.88635337e-01 2.95136869e-01 6.22464895e-01 -3.87739539e-01 -8.15906167e-01 1.24952960e+00 4.56209570e-01 -3.36710699e-02 5.76992154e-01 -1.23440969e+00 -1.09321642e+00 5.16456783e-01 -7.71395743e-01 2.78269410e-01 4.48540926e-01 2.72536486e-01 8.33680689e-01 -1.01982081e+00 7.51265526e-01 1.22065330e+00 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6.20333076e-01 -8.42399001e-01 -2.71748632e-01 -8.01254749e-01 -3.95712435e-01 8.30067217e-01 5.37759185e-01 -7.81111062e-01 -2.05375850e-01 1.82552204e-01]	[8.473236083984375, 10.62991714477539]
a10efc8f-3ce4-4b49-a546-c4dcc287ad11	clue-a-chinese-language-understanding	2004.05986	null	https://arxiv.org/abs/2004.05986v3	https://arxiv.org/pdf/2004.05986v3.pdf	CLUE: A Chinese Language Understanding Evaluation Benchmark	The advent of natural language understanding (NLU) benchmarks for English, such as GLUE and SuperGLUE allows new NLU models to be evaluated across a diverse set of tasks. These comprehensive benchmarks have facilitated a broad range of research and applications in natural language processing (NLP). The problem, however, is that most such benchmarks are limited to English, which has made it difficult to replicate many of the successes in English NLU for other languages. To help remedy this issue, we introduce the first large-scale Chinese Language Understanding Evaluation (CLUE) benchmark. CLUE is an open-ended, community-driven project that brings together 9 tasks spanning several well-established single-sentence/sentence-pair classification tasks, as well as machine reading comprehension, all on original Chinese text. To establish results on these tasks, we report scores using an exhaustive set of current state-of-the-art pre-trained Chinese models (9 in total). We also introduce a number of supplementary datasets and additional tools to help facilitate further progress on Chinese NLU. Our benchmark is released at https://www.CLUEbenchmarks.com	['Kyle Richardson', 'Zhe Zhao', 'Lu Li', 'Weijian Xie', 'Qianqian Dong', 'Yechen Xu', 'Zuoyu Tian', 'Zhenzhong Lan', 'Rongzhao Wang', 'Liang Xu', 'Junyi Li', 'He Zhou', 'Chenjie Cao', 'Zhengliang Yang', 'Yudong Li', 'Yiwen Zhang', 'Yiming Cui', 'Xuanwei Zhang', 'Weitang Liu', 'Jun Zeng', 'Cong Yue', 'Yin Tian', 'Shaoweihua Liu', 'Qipeng Zhao', 'Kai Sun', 'Hai Hu', 'Dian Yu', 'Yina Patterson', 'Yanting Li', 'Xinrui Zhang', 'Cong Yu', 'Bo Shi']	2020-04-13	null	https://aclanthology.org/2020.coling-main.419	https://aclanthology.org/2020.coling-main.419.pdf	coling-2020-8	['sentence-pair-classification']	['natural-language-processing']	[ 4.36894745e-01 -3.83823328e-02 -2.20981613e-01 -5.60155272e-01 -1.37430596e+00 -9.08298314e-01 5.21342516e-01 3.56772184e-01 -6.73303068e-01 7.78531492e-01 6.96785927e-01 -7.27406085e-01 4.92441386e-01 -4.96840596e-01 -8.15208852e-01 4.43452038e-02 1.45601839e-01 5.97763181e-01 -1.13637093e-02 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75c930e9-688d-4f47-8d31-76aed7248613	chord-recognition-music-and-audio-information	2105.07019	null	https://arxiv.org/abs/2105.07019v2	https://arxiv.org/pdf/2105.07019v2.pdf	Chord Recognition- Music and Audio Information Retrieval	Music Information Retrieval (MIR) is a collaborative scientific study that help to build innovative information research themes, novel frameworks, and developing connected delivery mechanisms in addition to making the world's massive collection of music open for everyone. Modern rock music proved to be difficult to estimate tempo and chord recognition did not work. All of the findings indicate that modern rock and metal music can be analysed, despite its complexity, but that further research is needed in this area to make it useful. Using a neural network has been one of the simplest ways of dealing with it. The pitch class profile vector is used in the neural network method. Because the vector only contains 12 elements of semi-tone values, it is enough for chord recognition. Of course, there are other ways of achieving this work, most of them depend on pitch class profiling to transform the chord into a type that can be recognised, but the recognition process is time-consuming centred on extremely complicated and memory-intensive methods.	['Shah Riya Chiragkumar']	2021-05-14	null	null	null	null	['chord-recognition', 'music-information-retrieval']	['audio', 'music']	[ 2.23070845e-01 -4.42206085e-01 -3.57121021e-01 9.78506058e-02 -4.10832077e-01 -8.16715777e-01 2.45298624e-01 7.35390931e-02 -4.27687705e-01 2.60447949e-01 1.86058164e-01 -2.68041760e-01 -5.91225505e-01 -8.13882649e-01 9.79439840e-02 -7.83890367e-01 7.00616241e-02 4.16909277e-01 3.16911489e-01 -4.50249970e-01 9.67325270e-01 5.40411651e-01 -1.96922672e+00 2.93291211e-01 2.43174091e-01 8.72677386e-01 1.60620958e-01 6.91771388e-01 -4.51714665e-01 7.20812142e-01 -7.95236766e-01 -2.84431934e-01 2.51698703e-01 -7.91726708e-01 -9.42475379e-01 -7.31155574e-01 -2.13684570e-02 9.72279310e-02 1.76711723e-01 8.93799067e-01 6.54028893e-01 3.97298604e-01 6.25512242e-01 -7.84058690e-01 -1.59871861e-01 9.80454981e-01 -1.54900700e-01 6.81740195e-02 5.83297312e-01 -4.28026229e-01 9.38121617e-01 -5.28709948e-01 4.48859006e-01 6.84228957e-01 9.17232752e-01 1.91340387e-01 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a1b7eff0-e992-401b-ae80-e36271034f85	investigations-in-audio-captioning-addressing	2211.06547	null	https://arxiv.org/abs/2211.06547v2	https://arxiv.org/pdf/2211.06547v2.pdf	Investigations in Audio Captioning: Addressing Vocabulary Imbalance and Evaluating Suitability of Language-Centric Performance Metrics	The analysis, processing, and extraction of meaningful information from sounds all around us is the subject of the broader area of audio analytics. Audio captioning is a recent addition to the domain of audio analytics, a cross-modal translation task that focuses on generating natural descriptions from sound events occurring in an audio stream. In this work, we identify and improve on three main challenges in automated audio captioning: i) data scarcity, ii) imbalance or limitations in the audio captions vocabulary, and iii) the proper performance evaluation metric that can best capture both auditory and semantic characteristics. We find that generally adopted loss functions can result in an unfair vocabulary imbalance during model training. We propose two audio captioning augmentation methods that enrich the training dataset and the vocabulary size. We further underline the need for in-domain pretraining by exploring the suitability of audio encoders that were previously trained on different audio tasks. Finally, we systematically explore five performance metrics borrowed from the image captioning domain and highlight their limitations for the audio domain.	['Dimitra Emmanouilidou', 'Sandeep Kothinti']	2022-11-12	null	null	null	null	['audio-captioning']	['audio']	[ 9.02436554e-01 1.76507175e-01 2.59497792e-01 -2.43303820e-01 -1.36296785e+00 -4.69745755e-01 3.99098098e-01 3.54121923e-01 -3.09674948e-01 5.98499179e-01 6.99794948e-01 4.48307209e-02 -1.30814284e-01 -4.05112505e-01 -8.91268671e-01 -2.19178230e-01 -6.52133226e-02 3.78707826e-01 -2.98044868e-02 -1.36838257e-01 -1.59205478e-02 -4.72796746e-02 -2.10571527e+00 5.26415348e-01 5.12522280e-01 1.41569304e+00 9.18907449e-02 9.13518965e-01 -2.35484511e-01 7.73787379e-01 -7.20428705e-01 -2.73589045e-01 -4.25369516e-02 -4.68706459e-01 -8.65286171e-01 -1.21554643e-01 5.88562250e-01 5.80394454e-02 -1.41623527e-01 8.50775719e-01 8.74599576e-01 7.17407316e-02 6.23496413e-01 -1.53202844e+00 -4.11728442e-01 9.47631538e-01 7.45389014e-02 4.19129878e-01 6.33696496e-01 2.90276308e-04 1.10734200e+00 -8.22221816e-01 3.95237863e-01 1.03435910e+00 6.56510532e-01 4.47179943e-01 -1.00528014e+00 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5b4d144d-64cb-46df-8711-90af78a63e49	towards-mitigating-the-problem-of	2210.11194	null	https://arxiv.org/abs/2210.11194v1	https://arxiv.org/pdf/2210.11194v1.pdf	Towards Mitigating the Problem of Insufficient and Ambiguous Supervision in Online Crowdsourcing Annotation	In real-world crowdsourcing annotation systems, due to differences in user knowledge and cultural backgrounds, as well as the high cost of acquiring annotation information, the supervision information we obtain might be insufficient and ambiguous. To mitigate the negative impacts, in this paper, we investigate a more general and broadly applicable learning problem, i.e. \emph{semi-supervised partial label learning}, and propose a novel method based on pseudo-labeling and contrastive learning. Following the key inventing principle, our method facilitate the partial label disambiguation process with unlabeled data and at the same time assign reliable pseudo-labels to weakly supervised examples. Specifically, our method learns from the ambiguous labeling information via partial cross-entropy loss. Meanwhile, high-accuracy pseudo-labels are generated for both partial and unlabeled examples through confidence-based thresholding and contrastive learning is performed in a hybrid unsupervised and supervised manner for more discriminative representations, while its supervision increases curriculumly. The two main components systematically work as a whole and reciprocate each other. In experiments, our method consistently outperforms all comparing methods by a significant margin and set up the first state-of-the-art performance for semi-supervised partial label learning on image benchmarks.	['Shu-Tao Xia', 'Zimo Liu', 'Tianxiang Li', 'Mingyan Zhu', 'Bowen Zhao', 'Qian-Wei Wang']	2022-10-20	null	null	null	null	['partial-label-learning']	['methodology']	[ 4.45177525e-01 2.29220644e-01 -4.49813664e-01 -7.35904515e-01 -1.17739046e+00 -7.39571273e-01 5.04417837e-01 1.28095835e-01 -5.59128165e-01 9.73527312e-01 -1.40305795e-02 2.08288476e-01 2.80484289e-01 -2.41524339e-01 -6.05024815e-01 -8.95980000e-01 5.87681890e-01 5.35850585e-01 3.08458030e-01 -1.28117334e-02 -2.94961426e-02 -8.43847245e-02 -1.49192047e+00 2.51161188e-01 1.12647784e+00 1.03836739e+00 1.23238705e-01 1.05105489e-02 -1.59167483e-01 9.10720170e-01 -3.91840219e-01 -6.89940393e-01 2.75807947e-01 -4.06018376e-01 -1.02279305e+00 6.44529104e-01 4.53577608e-01 -1.04500487e-01 2.29206845e-01 1.22849369e+00 5.39041698e-01 3.75990919e-03 6.57782257e-01 -1.18904686e+00 -6.53052032e-01 5.34118891e-01 -8.17364395e-01 -2.98372537e-01 2.77918339e-01 1.45109668e-01 1.13191676e+00 -1.19453597e+00 3.96583706e-01 1.05401051e+00 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d947263f-a4d3-46c8-a993-79f7b8a639e6	memory-guided-collaborative-attention-for	2208.02960	null	https://arxiv.org/abs/2208.02960v1	https://arxiv.org/pdf/2208.02960v1.pdf	Memory-Guided Collaborative Attention for Nighttime Thermal Infrared Image Colorization	Nighttime thermal infrared (NTIR) image colorization, also known as translation of NTIR images into daytime color images (NTIR2DC), is a promising research direction to facilitate nighttime scene perception for humans and intelligent systems under unfavorable conditions (e.g., complete darkness). However, previously developed methods have poor colorization performance for small sample classes. Moreover, reducing the high confidence noise in pseudo-labels and addressing the problem of image gradient disappearance during translation are still under-explored, and keeping edges from being distorted during translation is also challenging. To address the aforementioned issues, we propose a novel learning framework called Memory-guided cOllaboRative atteNtion Generative Adversarial Network (MornGAN), which is inspired by the analogical reasoning mechanisms of humans. Specifically, a memory-guided sample selection strategy and adaptive collaborative attention loss are devised to enhance the semantic preservation of small sample categories. In addition, we propose an online semantic distillation module to mine and refine the pseudo-labels of NTIR images. Further, conditional gradient repair loss is introduced for reducing edge distortion during translation. Extensive experiments on the NTIR2DC task show that the proposed MornGAN significantly outperforms other image-to-image translation methods in terms of semantic preservation and edge consistency, which helps improve the object detection accuracy remarkably.	['Yong-Jie Li', 'Kai-Fu Yang', 'Yi-Jun Cao', 'Fu-Ya Luo']	2022-08-05	null	null	null	null	['colorization']	['computer-vision']	[ 4.68418479e-01 -3.90493125e-01 9.92157310e-02 -3.52426201e-01 -4.61281925e-01 -4.39958036e-01 3.35145026e-01 -5.26909053e-01 -3.87754858e-01 6.08847857e-01 -4.71906178e-03 -2.69854873e-01 -3.51897404e-02 -6.87158823e-01 -6.79390132e-01 -1.06918430e+00 6.81657672e-01 -1.48203641e-01 -5.38401585e-03 -1.74274072e-01 3.38869125e-01 2.55095690e-01 -1.29286301e+00 1.02584109e-01 1.41933918e+00 9.88298535e-01 3.74188840e-01 3.95312577e-01 -1.63472388e-02 7.73277998e-01 -3.28018129e-01 -3.96921784e-01 4.17110771e-01 -5.09862542e-01 -3.47096473e-01 3.30409378e-01 5.29347599e-01 -3.79270017e-01 -4.66656923e-01 1.47680283e+00 5.95969379e-01 5.67380190e-01 3.68559361e-01 -1.36375582e+00 -1.47958434e+00 9.29079652e-02 -7.43430972e-01 2.14538798e-01 -1.11753508e-01 2.27886334e-01 4.61375386e-01 -1.08404541e+00 6.37397468e-02 1.16406643e+00 5.23626268e-01 5.14787436e-01 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509220a5-e973-4d30-bb08-ba4b5fab1229	shadow-removal-by-a-lightness-guided-network	2006.15617	null	https://arxiv.org/abs/2006.15617v1	https://arxiv.org/pdf/2006.15617v1.pdf	Shadow Removal by a Lightness-Guided Network with Training on Unpaired Data	Shadow removal can significantly improve the image visual quality and has many applications in computer vision. Deep learning methods based on CNNs have become the most effective approach for shadow removal by training on either paired data, where both the shadow and underlying shadow-free versions of an image are known, or unpaired data, where shadow and shadow-free training images are totally different with no correspondence. In practice, CNN training on unpaired data is more preferred given the easiness of training data collection. In this paper, we present a new Lightness-Guided Shadow Removal Network (LG-ShadowNet) for shadow removal by training on unpaired data. In this method, we first train a CNN module to compensate for the lightness and then train a second CNN module with the guidance of lightness information from the first CNN module for final shadow removal. We also introduce a loss function to further utilise the colour prior of existing data. Extensive experiments on widely used ISTD, adjusted ISTD and USR datasets demonstrate that the proposed method outperforms the state-of-the-art methods with training on unpaired data.	['Song Wang', 'Yang Mi', 'Mengyang Pu', 'Zhihao Liu', 'Hui Yin']	2020-06-28	null	null	null	null	['shadow-removal']	['computer-vision']	[ 6.69017613e-01 3.16137671e-02 6.00044668e-01 -4.78746653e-01 -1.31044775e-01 -1.93359032e-01 3.60890001e-01 -4.11088794e-01 -3.95765334e-01 8.16482842e-01 -2.10238859e-01 -4.97066438e-01 4.94869828e-01 -5.12310803e-01 -6.33451998e-01 -1.06208837e+00 2.88000584e-01 1.28097028e-01 6.87319517e-01 -2.68948585e-01 7.65016750e-02 4.27073598e-01 -1.45106578e+00 -2.31845863e-02 1.10701239e+00 1.13676178e+00 7.68534005e-01 5.70796251e-01 -6.01776727e-02 5.60059011e-01 -7.11135268e-01 -3.63321938e-02 6.29908919e-01 -5.13377368e-01 -6.94065541e-02 9.43783298e-02 6.07079148e-01 -7.57753551e-01 -6.17974520e-01 6.69905901e-01 6.90281034e-01 2.24423975e-01 2.74325043e-01 -1.25748873e+00 -6.72886908e-01 -3.01757365e-01 -6.85995221e-01 4.50258590e-02 3.51283997e-02 1.24659240e-01 4.62453067e-01 -8.52775872e-01 2.12692618e-01 1.02466130e+00 6.90171540e-01 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1b2fec55-d7fc-4ec5-8f62-3f2fe61a1ce0	an-efficient-point-of-gaze-estimator-for-low	2106.05106	null	https://arxiv.org/abs/2106.05106v1	https://arxiv.org/pdf/2106.05106v1.pdf	An Efficient Point of Gaze Estimator for Low-Resolution Imaging Systems Using Extracted Ocular Features Based Neural Architecture	A user's eyes provide means for Human Computer Interaction (HCI) research as an important modal. The time to time scientific explorations of the eye has already seen an upsurge of the benefits in HCI applications from gaze estimation to the measure of attentiveness of a user looking at a screen for a given time period. The eye tracking system as an assisting, interactive tool can be incorporated by physically disabled individuals, fitted best for those who have eyes as only a limited set of communication. The threefold objective of this paper is - 1. To introduce a neural network based architecture to predict users' gaze at 9 positions displayed in the 11.31{\deg} visual range on the screen, through a low resolution based system such as a webcam in real time by learning various aspects of eyes as an ocular feature set. 2.A collection of coarsely supervised feature set obtained in real time which is also validated through the user case study presented in the paper for 21 individuals ( 17 men and 4 women ) from whom a 35k set of instances was derived with an accuracy score of 82.36% and f1_score of 82.2% and 3.A detailed study over applicability and underlying challenges of such systems. The experimental results verify the feasibility and validity of the proposed eye gaze tracking model.	['Kavi Arya', 'Imon Mukherjee', 'Atul Sahay']	2021-06-09	null	null	null	null	['gaze-estimation']	['computer-vision']	[ 1.60916314e-01 3.28429610e-01 1.54744938e-01 -3.23849499e-01 2.71413714e-01 -1.14040360e-01 -2.26119589e-02 -4.53921556e-01 -3.73754829e-01 6.71009004e-01 -1.33161634e-01 -2.37820566e-01 -5.61011195e-01 1.06702875e-02 -1.69953659e-01 -4.64226276e-01 5.06719761e-02 -1.50568336e-01 2.92930868e-03 -2.20600054e-01 5.49892485e-01 4.21261996e-01 -2.62094784e+00 -3.84656750e-02 8.11615884e-01 1.13489556e+00 6.57523274e-02 9.46000099e-01 4.37521160e-01 3.56025219e-01 -7.60904014e-01 2.50234187e-01 1.42095283e-01 -1.49958163e-01 -4.69806373e-01 1.51779234e-01 8.02481413e-01 -3.24745834e-01 2.02288732e-01 6.83560908e-01 8.18272412e-01 8.95119160e-02 5.38426578e-01 -1.54265451e+00 -5.84327340e-01 -4.77236927e-01 -6.66814506e-01 3.86712015e-01 9.13355291e-01 3.53388160e-01 2.44905576e-01 -6.71988904e-01 2.42689863e-01 9.83792722e-01 5.13948023e-01 6.51037157e-01 -8.69994998e-01 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1.05333060e-01 8.60287488e-01 -9.69256699e-01 -4.15734291e-01 4.78601307e-01]	[14.054485321044922, 0.17055562138557434]
be214286-8644-445b-bf61-543fc7fa0243	framewise-approach-in-multimodal-emotion	1805.01369	null	http://arxiv.org/abs/1805.01369v1	http://arxiv.org/pdf/1805.01369v1.pdf	Framewise approach in multimodal emotion recognition in OMG challenge	In this report we described our approach achieves $53\%$ of unweighted accuracy over $7$ emotions and $0.05$ and $0.09$ mean squared errors for arousal and valence in OMG emotion recognition challenge. Our results were obtained with ensemble of single modality models trained on voice and face data from video separately. We consider each stream as a sequence of frames. Next we estimated features from frames and handle it with recurrent neural network. As audio frame we mean short $0.4$ second spectrogram interval. For features estimation for face pictures we used own ResNet neural network pretrained on AffectNet database. Each short spectrogram was considered as a picture and processed by convolutional network too. As a base audio model we used ResNet pretrained in speaker recognition task. Predictions from both modalities were fused on decision level and improve single-channel approaches by a few percent	['Maxim Ryabov', 'Grigoriy Sterling', 'Andrey Belyaev']	2018-05-03	null	null	null	null	['multimodal-emotion-recognition', 'multimodal-emotion-recognition']	['computer-vision', 'speech']	[ 1.98118150e-01 4.90858465e-01 3.31668526e-01 -9.68432069e-01 -9.14170980e-01 -2.03123808e-01 3.43888015e-01 -1.28783107e-01 -5.03328800e-01 7.27673531e-01 3.14824820e-01 4.59136248e-01 3.47015083e-01 -5.58428347e-01 -7.02199519e-01 -3.89014274e-01 -4.17765945e-01 -2.98635185e-01 -6.88851297e-01 -3.13339889e-01 -3.86569798e-02 3.50017101e-01 -1.71423101e+00 7.70908594e-01 -1.39499471e-01 2.12414169e+00 -5.87620318e-01 1.05312395e+00 1.36643846e-03 9.84286070e-01 -6.43746078e-01 -5.33055544e-01 2.88803250e-01 -3.42232287e-01 -6.14036679e-01 1.80482659e-02 4.89043683e-01 -2.00130835e-01 -2.94231087e-01 8.00035059e-01 9.11881328e-01 3.58609706e-01 5.52933693e-01 -1.37611496e+00 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8bdb0192-7743-4924-b628-fcc7d592122f	unsupervised-features-extraction-for-binary	1810.09683	null	http://arxiv.org/abs/1810.09683v2	http://arxiv.org/pdf/1810.09683v2.pdf	Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks	In this paper we consider the binary similarity problem that consists in determining if two binary functions are similar only considering their compiled form. This problem is know to be crucial in several application scenarios, such as copyright disputes, malware analysis, vulnerability detection, etc. The current state-of-the-art solutions in this field work by creating an embedding model that maps binary functions into vectors in $\mathbb{R}^{n}$. Such embedding model captures syntactic and semantic similarity between binaries, i.e., similar binary functions are mapped to points that are close in the vector space. This strategy has many advantages, one of them is the possibility to precompute embeddings of several binary functions, and then compare them with simple geometric operations (e.g., dot product). In [32] functions are first transformed in Annotated Control Flow Graphs (ACFGs) constituted by manually engineered features and then graphs are embedded into vectors using a deep neural network architecture. In this paper we propose and test several ways to compute annotated control flow graphs that use unsupervised approaches for feature learning, without incurring a human bias. Our methods are inspired after techniques used in the natural language processing community (e.g., we use word2vec to encode assembly instructions). We show that our approach is indeed successful, and it leads to better performance than previous state-of-the-art solutions. Furthermore, we report on a qualitative analysis of functions embeddings. We found interesting cases in which embeddings are clustered according to the semantic of the original binary function.	['Luca Massarelli', 'Roberto Baldoni', 'Leonardo Querzoni', 'Giuseppe Antonio Di Luna', 'Fabio Petroni']	2018-10-23	null	null	null	null	['vulnerability-detection']	['miscellaneous']	[ 9.99958888e-02 -1.61516801e-01 -1.19879909e-01 -3.32136393e-01 -1.52123123e-02 -1.06971717e+00 7.95517683e-01 7.19109952e-01 -5.33108652e-01 3.60083729e-01 1.45346923e-02 -6.84436440e-01 -2.25068286e-01 -1.23512566e+00 -8.22163999e-01 -4.93268669e-01 -1.76958337e-01 3.85354698e-01 2.37373397e-01 -5.95752478e-01 6.04982197e-01 7.17925131e-01 -1.65014207e+00 9.00622159e-02 6.47286832e-01 9.01293755e-01 -1.28572688e-01 6.70576572e-01 -3.63303244e-01 6.36485696e-01 -7.90403187e-01 -8.07850957e-01 2.07649261e-01 -2.69954741e-01 -7.91189373e-01 -2.85237074e-01 1.42870441e-01 1.14497513e-01 -3.56400371e-01 1.26524341e+00 -5.72838224e-02 1.31530121e-01 7.50120938e-01 -1.45650136e+00 -9.42932904e-01 4.67018455e-01 -3.28890495e-02 8.04703906e-02 4.22682464e-01 1.10061504e-01 1.26039052e+00 -3.80483478e-01 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dfaed6ca-f738-4900-a7fb-21ddd8ab6fe5	learning-diverse-stochastic-human-action	1912.10150	null	https://arxiv.org/abs/1912.10150v1	https://arxiv.org/pdf/1912.10150v1.pdf	Learning Diverse Stochastic Human-Action Generators by Learning Smooth Latent Transitions	Human-motion generation is a long-standing challenging task due to the requirement of accurately modeling complex and diverse dynamic patterns. Most existing methods adopt sequence models such as RNN to directly model transitions in the original action space. Due to high dimensionality and potential noise, such modeling of action transitions is particularly challenging. In this paper, we focus on skeleton-based action generation and propose to model smooth and diverse transitions on a latent space of action sequences with much lower dimensionality. Conditioned on a latent sequence, actions are generated by a frame-wise decoder shared by all latent action-poses. Specifically, an implicit RNN is defined to model smooth latent sequences, whose randomness (diversity) is controlled by noise from the input. Different from standard action-prediction methods, our model can generate action sequences from pure noise without any conditional action poses. Remarkably, it can also generate unseen actions from mixed classes during training. Our model is learned with a bi-directional generative-adversarial-net framework, which not only can generate diverse action sequences of a particular class or mix classes, but also learns to classify action sequences within the same model. Experimental results show the superiority of our method in both diverse action-sequence generation and classification, relative to existing methods.	['Yufan Zhou', 'Changyou Chen', 'Zhenyi Wang', 'Ping Yu', 'Yang Zhao', 'Ruiyi Zhang', 'Junsong Yuan']	2019-12-21	learning-diverse-stochastic-human-action-1	null	null	aaai-2019-12	['human-action-generation', 'action-generation']	['computer-vision', 'computer-vision']	[ 8.41231048e-01 1.47002280e-01 -3.25266093e-01 1.21514753e-01 -9.53791440e-01 -4.24653053e-01 9.09739614e-01 -9.53896582e-01 7.22774640e-02 8.61906946e-01 6.24027073e-01 9.00149047e-02 3.40156525e-01 -8.35716307e-01 -9.70840156e-01 -9.03773427e-01 4.30186272e-01 4.83764023e-01 2.12792009e-01 -1.59815356e-01 -1.18247211e-01 1.91506594e-01 -1.41003251e+00 5.24350107e-01 7.29536772e-01 5.89512110e-01 1.69470489e-01 9.30748224e-01 8.08500499e-02 1.36554778e+00 -5.46721637e-01 -1.72915533e-01 2.86060661e-01 -1.12941253e+00 -5.67540526e-01 4.59480017e-01 1.97466925e-01 -5.08005619e-01 -7.45052993e-01 7.46201396e-01 4.82046634e-01 3.72268468e-01 8.57946992e-01 -1.37847185e+00 -5.91629982e-01 4.94623244e-01 -1.92415446e-01 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70e5e0a6-3da9-4b91-8998-ed163552e836	specializing-multi-domain-nmt-via-penalizing	2210.12910	null	https://arxiv.org/abs/2210.12910v1	https://arxiv.org/pdf/2210.12910v1.pdf	Specializing Multi-domain NMT via Penalizing Low Mutual Information	Multi-domain Neural Machine Translation (NMT) trains a single model with multiple domains. It is appealing because of its efficacy in handling multiple domains within one model. An ideal multi-domain NMT should learn distinctive domain characteristics simultaneously, however, grasping the domain peculiarity is a non-trivial task. In this paper, we investigate domain-specific information through the lens of mutual information (MI) and propose a new objective that penalizes low MI to become higher. Our method achieved the state-of-the-art performance among the current competitive multi-domain NMT models. Also, we empirically show our objective promotes low MI to be higher resulting in domain-specialized multi-domain NMT.	['Cheonbok Park', 'Edward Choi', 'Hyunchang Cho', 'Hantae Kim', 'Jiyoung Lee']	2022-10-24	null	null	null	null	['nmt']	['computer-code']	[ 4.86974806e-01 -8.89287665e-02 -5.24654269e-01 -4.06810284e-01 -1.08016860e+00 -6.92776263e-01 9.09892499e-01 -2.68391490e-01 -4.64907765e-01 9.60349798e-01 2.64378358e-02 -3.28923941e-01 -1.15492091e-01 -4.15638119e-01 -7.19926178e-01 -5.78354120e-01 3.23001951e-01 1.00721300e+00 1.54048413e-01 -3.55365247e-01 -5.67780063e-02 1.24010392e-01 -1.02012074e+00 4.01628852e-01 1.45590460e+00 7.08926022e-01 6.31911814e-01 7.30462670e-02 -1.29850566e-01 3.30734104e-01 -6.39783859e-01 -6.84406459e-01 3.26117516e-01 -7.60330260e-01 -8.00869584e-01 -1.40541077e-01 3.42454404e-01 4.80669737e-02 1.27474979e-01 1.15858459e+00 4.21285659e-01 -2.47077066e-02 9.84133840e-01 -1.33102775e+00 -9.94449615e-01 7.25749075e-01 -5.07967472e-01 2.65210178e-02 -2.43578833e-02 -2.87883896e-02 1.17870629e+00 -7.91233301e-01 8.85925531e-01 1.19826519e+00 3.01138341e-01 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a0c207ee-adce-4896-a9d0-a04bb038a37b	chinese-named-entity-recognition-augmented	1912.08282	null	https://arxiv.org/abs/1912.08282v2	https://arxiv.org/pdf/1912.08282v2.pdf	Chinese Named Entity Recognition Augmented with Lexicon Memory	Inspired by a concept of content-addressable retrieval from cognitive science, we propose a novel fragment-based model augmented with a lexicon-based memory for Chinese NER, in which both the character-level and word-level features are combined to generate better feature representations for possible name candidates. It is observed that locating the boundary information of entity names is useful in order to classify them into pre-defined categories. Position-dependent features, including prefix and suffix are introduced for NER in the form of distributed representation. The lexicon-based memory is used to help generate such position-dependent features and deal with the problem of out-of-vocabulary words. Experimental results showed that the proposed model, called LEMON, achieved state-of-the-art on four datasets.	['Xuanjing Huang', 'Yi Zhou', 'Xiaoqing Zheng']	2019-12-17	null	null	null	null	['chinese-named-entity-recognition']	['natural-language-processing']	[-2.80039221e-01 -4.00450647e-01 -1.19966544e-01 -4.01694365e-02 -8.31836939e-01 -6.78455055e-01 6.35957181e-01 4.90175247e-01 -8.64218831e-01 7.08680212e-01 5.42480409e-01 -1.26071423e-02 -5.14294624e-01 -1.16383410e+00 -1.32735536e-01 -3.19440424e-01 -2.33101360e-02 4.80109930e-01 4.33642983e-01 -4.79567647e-01 1.00181878e+00 5.59722781e-01 -1.71440196e+00 5.17399430e-01 1.16569257e+00 8.45901072e-01 4.81644660e-01 -5.20661026e-02 -1.11108732e+00 5.78590035e-01 -4.47720051e-01 -1.67637989e-01 -8.71006250e-02 -1.73980102e-01 -9.86422837e-01 -3.40971828e-01 -4.78523411e-02 1.72869220e-01 -2.77865708e-01 1.11497116e+00 3.82996380e-01 4.71174479e-01 7.68835247e-01 -3.32887292e-01 -9.64418769e-01 7.70577967e-01 -1.04127973e-01 4.30047512e-01 4.43787128e-01 -5.15572429e-01 1.11844027e+00 -1.18280423e+00 7.06555486e-01 1.27934313e+00 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0b823695-94b9-4741-8e2e-ed6eb802e339	occluded-human-mesh-recovery	2203.13349	null	https://arxiv.org/abs/2203.13349v1	https://arxiv.org/pdf/2203.13349v1.pdf	Occluded Human Mesh Recovery	Top-down methods for monocular human mesh recovery have two stages: (1) detect human bounding boxes; (2) treat each bounding box as an independent single-human mesh recovery task. Unfortunately, the single-human assumption does not hold in images with multi-human occlusion and crowding. Consequently, top-down methods have difficulties in recovering accurate 3D human meshes under severe person-person occlusion. To address this, we present Occluded Human Mesh Recovery (OCHMR) - a novel top-down mesh recovery approach that incorporates image spatial context to overcome the limitations of the single-human assumption. The approach is conceptually simple and can be applied to any existing top-down architecture. Along with the input image, we condition the top-down model on spatial context from the image in the form of body-center heatmaps. To reason from the predicted body centermaps, we introduce Contextual Normalization (CoNorm) blocks to adaptively modulate intermediate features of the top-down model. The contextual conditioning helps our model disambiguate between two severely overlapping human bounding-boxes, making it robust to multi-person occlusion. Compared with state-of-the-art methods, OCHMR achieves superior performance on challenging multi-person benchmarks like 3DPW, CrowdPose and OCHuman. Specifically, our proposed contextual reasoning architecture applied to the SPIN model with ResNet-50 backbone results in 75.2 PMPJPE on 3DPW-PC, 23.6 AP on CrowdPose and 37.7 AP on OCHuman datasets, a significant improvement of 6.9 mm, 6.4 AP and 20.8 AP respectively over the baseline. Code and models will be released.	['Kris Kitani', 'Shashank Tripathi', 'Rawal Khirodkar']	2022-03-24	null	http://openaccess.thecvf.com//content/CVPR2022/html/Khirodkar_Occluded_Human_Mesh_Recovery_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Khirodkar_Occluded_Human_Mesh_Recovery_CVPR_2022_paper.pdf	cvpr-2022-1	['human-mesh-recovery']	['computer-vision']	[ 2.19121054e-01 2.47214511e-01 6.74964488e-02 -1.95122391e-01 -7.19594777e-01 -2.72331890e-02 3.26585680e-01 -1.90655112e-01 -2.80310273e-01 6.43883526e-01 3.93694431e-01 2.54182398e-01 1.99616596e-01 -7.81138897e-01 -8.85414958e-01 -4.55765039e-01 3.20962310e-01 8.59072864e-01 6.83013260e-01 -4.27274138e-01 -2.64646020e-02 4.64410454e-01 -1.78036809e+00 4.12858367e-01 7.07468569e-01 7.87876368e-01 -1.24744214e-01 5.96031070e-01 2.24439546e-01 5.91472566e-01 -5.10657132e-01 -3.88131350e-01 4.06053066e-01 -1.03042156e-01 -8.11062992e-01 6.72183409e-02 1.19404662e+00 -3.92086118e-01 -3.74723226e-01 7.25256801e-01 8.20221961e-01 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973274a2-4bdc-4cf1-9185-76c03c550d1f	multimodal-semi-supervised-learning-for-text	2205.03873	null	https://arxiv.org/abs/2205.03873v1	https://arxiv.org/pdf/2205.03873v1.pdf	Multimodal Semi-Supervised Learning for Text Recognition	Until recently, the number of public real-world text images was insufficient for training scene text recognizers. Therefore, most modern training methods rely on synthetic data and operate in a fully supervised manner. Nevertheless, the amount of public real-world text images has increased significantly lately, including a great deal of unlabeled data. Leveraging these resources requires semi-supervised approaches; however, the few existing methods do not account for vision-language multimodality structure and therefore suboptimal for state-of-the-art multimodal architectures. To bridge this gap, we present semi-supervised learning for multimodal text recognizers (SemiMTR) that leverages unlabeled data at each modality training phase. Notably, our method refrains from extra training stages and maintains the current three-stage multimodal training procedure. Our algorithm starts by pretraining the vision model through a single-stage training that unifies self-supervised learning with supervised training. More specifically, we extend an existing visual representation learning algorithm and propose the first contrastive-based method for scene text recognition. After pretraining the language model on a text corpus, we fine-tune the entire network via a sequential, character-level, consistency regularization between weakly and strongly augmented views of text images. In a novel setup, consistency is enforced on each modality separately. Extensive experiments validate that our method outperforms the current training schemes and achieves state-of-the-art results on multiple scene text recognition benchmarks.	['Ron Litman', 'Shai Mazor', 'Roy Ganz', 'Aviad Aberdam']	2022-05-08	null	null	null	null	['scene-text-recognition']	['computer-vision']	[ 6.51075780e-01 -2.06482723e-01 -3.86854351e-01 -4.46319699e-01 -9.09380257e-01 -5.56088448e-01 1.09699023e+00 -1.60455346e-01 -5.84928572e-01 3.44816476e-01 9.11067650e-02 -4.65908617e-01 4.86916125e-01 -4.36612308e-01 -8.26812029e-01 -5.74577451e-01 7.59351671e-01 7.14760125e-01 1.18841365e-01 -1.65324688e-01 1.01112396e-01 1.44749895e-01 -1.47352397e+00 6.98012769e-01 1.00496829e+00 8.24436069e-01 2.30353937e-01 6.69607937e-01 -3.88117552e-01 1.05877817e+00 -7.57396668e-02 -5.90071738e-01 8.49815365e-03 -4.38682258e-01 -8.60126913e-01 8.10274124e-01 7.17372060e-01 -3.82173687e-01 -6.05294764e-01 9.64010179e-01 2.69684166e-01 6.10010475e-02 6.84463799e-01 -1.15164804e+00 -8.64823461e-01 5.10058343e-01 -6.74352288e-01 -3.24289680e-01 3.39506477e-01 2.46694997e-01 1.02173316e+00 -1.37379611e+00 5.60049772e-01 1.04297864e+00 4.71317053e-01 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22c013d8-f01a-436b-8fd1-8488dcada0a4	union-visual-translation-embedding-for-visual	1905.11624	null	https://arxiv.org/abs/1905.11624v3	https://arxiv.org/pdf/1905.11624v3.pdf	Contextual Translation Embedding for Visual Relationship Detection and Scene Graph Generation	Relations amongst entities play a central role in image understanding. Due to the complexity of modeling (subject, predicate, object) relation triplets, it is crucial to develop a method that can not only recognize seen relations, but also generalize to unseen cases. Inspired by a previously proposed visual translation embedding model, or VTransE, we propose a context-augmented translation embedding model that can capture both common and rare relations. The previous VTransE model maps entities and predicates into a low-dimensional embedding vector space where the predicate is interpreted as a translation vector between the embedded features of the bounding box regions of the subject and the object. Our model additionally incorporates the contextual information captured by the bounding box of the union of the subject and the object, and learns the embeddings guided by the constraint predicate $\approx$ union (subject, object) $-$ subject $-$ object. In a comprehensive evaluation on multiple challenging benchmarks, our approach outperforms previous translation-based models and comes close to or exceeds the state of the art across a range of settings, from small-scale to large-scale datasets, from common to previously unseen relations. It also achieves promising results for the recently introduced task of scene graph generation.	['Zih-Siou Hung', 'Svetlana Lazebnik', 'Arun Mallya']	2019-05-28	null	null	null	null	['visual-relationship-detection']	['computer-vision']	[ 3.55916172e-01 2.30404675e-01 -1.96854606e-01 -3.52062076e-01 -3.56063277e-01 -4.33554530e-01 8.60914350e-01 4.18239266e-01 -2.47900158e-01 3.54983747e-01 4.19536263e-01 -3.44261676e-01 -3.78151461e-02 -1.00016499e+00 -9.54776525e-01 -5.12967885e-01 -3.98561172e-02 6.37869656e-01 3.38397712e-01 -1.72165066e-01 -2.77101845e-01 3.76269281e-01 -1.48070240e+00 3.48035604e-01 6.22602165e-01 1.13520062e+00 -1.13920439e-02 1.96730882e-01 -1.50082767e-01 6.29009604e-01 -3.32335055e-01 -7.38517284e-01 1.92928717e-01 -2.89900184e-01 -1.01089191e+00 1.00481637e-01 6.88288271e-01 7.65471384e-02 -5.37415683e-01 8.93135726e-01 1.80933073e-01 2.34136596e-01 8.25793386e-01 -1.48258841e+00 -1.31781626e+00 3.13601375e-01 -5.70519328e-01 1.37709573e-01 5.44628561e-01 -2.43319407e-01 1.54631329e+00 -1.01642573e+00 1.10775387e+00 1.28754103e+00 2.46850610e-01 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39d9eed7-6bb5-45e2-80f7-4f6e066b824c	learning-to-transfer-examples-for-partial	1903.12230	null	http://arxiv.org/abs/1903.12230v2	http://arxiv.org/pdf/1903.12230v2.pdf	Learning to Transfer Examples for Partial Domain Adaptation	Domain adaptation is critical for learning in new and unseen environments. With domain adversarial training, deep networks can learn disentangled and transferable features that effectively diminish the dataset shift between the source and target domains for knowledge transfer. In the era of Big Data, the ready availability of large-scale labeled datasets has stimulated wide interest in partial domain adaptation (PDA), which transfers a recognizer from a labeled large domain to an unlabeled small domain. It extends standard domain adaptation to the scenario where target labels are only a subset of source labels. Under the condition that target labels are unknown, the key challenge of PDA is how to transfer relevant examples in the shared classes to promote positive transfer, and ignore irrelevant ones in the specific classes to mitigate negative transfer. In this work, we propose a unified approach to PDA, Example Transfer Network (ETN), which jointly learns domain-invariant representations across the source and target domains, and a progressive weighting scheme that quantifies the transferability of source examples while controlling their importance to the learning task in the target domain. A thorough evaluation on several benchmark datasets shows that our approach achieves state-of-the-art results for partial domain adaptation tasks.	['Jian-Min Wang', 'Zhangjie Cao', 'Mingsheng Long', 'Qiang Yang', 'Kaichao You']	2019-03-28	learning-to-transfer-examples-for-partial-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Cao_Learning_to_Transfer_Examples_for_Partial_Domain_Adaptation_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Cao_Learning_to_Transfer_Examples_for_Partial_Domain_Adaptation_CVPR_2019_paper.pdf	cvpr-2019-6	['partial-domain-adaptation']	['methodology']	[ 5.85089147e-01 2.12062418e-01 -6.21455491e-01 -4.52203125e-01 -7.60389507e-01 -9.44251359e-01 5.19337833e-01 -1.03628121e-01 -4.16769087e-01 1.20018828e+00 5.46665564e-02 3.87387276e-02 -8.92970264e-02 -9.52843845e-01 -7.69657969e-01 -8.52386773e-01 2.02409998e-01 7.70203650e-01 2.48204753e-01 -4.23598230e-01 -2.82864600e-01 4.82319534e-01 -9.94900703e-01 3.62751871e-01 9.02326167e-01 8.49606335e-01 -9.43209156e-02 1.73619777e-01 -1.58226833e-01 6.43057227e-01 -7.02991128e-01 -3.49647075e-01 4.29724634e-01 -4.30510849e-01 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a71d3389-8867-464a-903f-4d5ee910397a	self-supervised-interest-point-detection-and	2306.01938	null	https://arxiv.org/abs/2306.01938v1	https://arxiv.org/pdf/2306.01938v1.pdf	Self-supervised Interest Point Detection and Description for Fisheye and Perspective Images	Keypoint detection and matching is a fundamental task in many computer vision problems, from shape reconstruction, to structure from motion, to AR/VR applications and robotics. It is a well-studied problem with remarkable successes such as SIFT, and more recent deep learning approaches. While great robustness is exhibited by these techniques with respect to noise, illumination variation, and rigid motion transformations, less attention has been placed on image distortion sensitivity. In this work, we focus on the case when this is caused by the geometry of the cameras used for image acquisition, and consider the keypoint detection and matching problem between the hybrid scenario of a fisheye and a projective image. We build on a state-of-the-art approach and derive a self-supervised procedure that enables training an interest point detector and descriptor network. We also collected two new datasets for additional training and testing in this unexplored scenario, and we demonstrate that current approaches are suboptimal because they are designed to work in traditional projective conditions, while the proposed approach turns out to be the most effective.	['Gianfranco Doretto', 'Yu Gu', 'Shivang Patel', 'Marcela Mera-Trujillo']	2023-06-02	null	null	null	null	['interest-point-detection', 'keypoint-detection']	['computer-vision', 'computer-vision']	[ 2.46140063e-01 -2.30261818e-01 -4.47780564e-02 -1.38537586e-01 -4.98872072e-01 -5.76389790e-01 9.42532480e-01 5.33297285e-02 -5.91952443e-01 2.15127960e-01 -2.78666884e-01 -1.91670768e-02 -2.37800121e-01 -6.12935781e-01 -8.11153054e-01 -6.00180984e-01 -7.56367743e-02 4.68389273e-01 5.43297946e-01 -2.76139528e-01 4.73647922e-01 9.80201304e-01 -1.75162804e+00 -4.44978029e-01 4.35655415e-01 9.24230933e-01 9.45083871e-02 5.52311540e-01 9.27766338e-02 4.33884412e-01 -3.15743536e-01 -4.70808089e-01 6.02870226e-01 -1.39506698e-01 -5.59395909e-01 1.31047964e-01 8.96283686e-01 -2.73647547e-01 -5.86912394e-01 1.01351893e+00 5.32336473e-01 1.39742166e-01 4.78904933e-01 -1.33861494e+00 -3.54150563e-01 -1.01540133e-01 -5.47662437e-01 1.49274606e-03 4.99065161e-01 -1.43990874e-01 7.82513380e-01 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8.63966718e-02 7.44762123e-01 -1.29071677e+00 -7.35928655e-01 -5.54890752e-01 -6.06001139e-01 4.98314947e-01 6.21271431e-01 -8.77882838e-01 -3.36536050e-01 -2.01090813e-01]	[7.837305068969727, -2.195344924926758]
d753a1ba-5b1e-4d62-849e-7622700956a1	dpdnet-a-robust-people-detector-using-deep	2006.01053	null	https://arxiv.org/abs/2006.01053v1	https://arxiv.org/pdf/2006.01053v1.pdf	DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera	In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability. Our neural network, called DPDnet, is based on two fully-convolutional encoder-decoder neural blocks based on residual layers. The Main Block takes a depth image as input and generates a pixel-wise confidence map, where each detected person in the image is represented by a Gaussian-like distribution. The refinement block combines the depth image and the output from the main block, to refine the confidence map. Both blocks are simultaneously trained end-to-end using depth images and head position labels. The experimental work shows that DPDNet outperforms state-of-the-art methods, with accuracies greater than 99% in three different publicly available datasets, without retraining not fine-tuning. In addition, the computational complexity of our proposal is independent of the number of people in the scene and runs in real time using conventional GPUs.	['Javier Macias-Guarasa', 'David Casillas-Perez', 'Cristina Losada-Gutierrez', 'Roberto Martin-Lopez', 'David Fuentes-Jimenez', 'Daniel Pizarro', 'Carlos A. Luna']	2020-06-01	null	null	null	null	['head-detection']	['computer-vision']	[-5.54712377e-02 3.07792783e-01 4.24472839e-01 -5.29257298e-01 -5.10249317e-01 9.37908143e-02 3.35755110e-01 2.61449605e-01 -9.08475518e-01 6.41636968e-01 -8.12922493e-02 2.84665853e-01 4.37060237e-01 -8.57987881e-01 -7.38381684e-01 -5.60335636e-01 -8.10671672e-02 8.11391354e-01 6.96998179e-01 1.96372971e-01 8.74583237e-03 2.08693355e-01 -1.82887578e+00 2.12200478e-01 5.71639001e-01 1.21933925e+00 1.15109667e-01 1.08003414e+00 3.85917276e-01 7.30988562e-01 -6.39634073e-01 -6.28903389e-01 2.92062908e-01 -2.78400481e-02 -3.37075353e-01 2.87646890e-01 7.04363406e-01 -7.97153115e-01 -5.97859979e-01 9.19618189e-01 9.36702609e-01 -2.30823755e-01 4.92066205e-01 -9.36925530e-01 -2.38451600e-01 1.19122289e-01 -7.98174500e-01 8.97215977e-02 4.75235015e-01 1.41146719e-01 3.31165701e-01 -9.07770872e-01 2.54797846e-01 1.45422614e+00 8.61696243e-01 5.77427685e-01 -9.42974508e-01 -7.38890529e-01 -1.48187364e-02 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52f10ba5-40bd-46c2-b156-969907d613d2	adaptkeybert-an-attention-based-approach	2211.07499	null	https://arxiv.org/abs/2211.07499v2	https://arxiv.org/pdf/2211.07499v2.pdf	AdaptKeyBERT: An Attention-Based approach towards Few-Shot & Zero-Shot Domain Adaptation of KeyBERT	Keyword extraction has been an important topic for modern natural language processing. With its applications ranging from ontology generation, fact verification in summarized text, and recommendation systems. While it has had significant data-intensive applications, it is often hampered when the data set is small. Downstream training for keyword extractors is a lengthy process and requires a significant amount of data. Recently, Few-shot Learning (FSL) and Zero-Shot Learning (ZSL) have been proposed to tackle this problem. Therefore, we propose AdaptKeyBERT, a pipeline for training keyword extractors with LLM bases by incorporating the concept of regularized attention into a pre-training phase for downstream domain adaptation. As we believe our work has implications to be utilized in the pipeline of FSL/ZSL and keyword extraction, we open-source our code as well as provide the fine-tuning library of the same name AdaptKeyBERT at https://github.com/AmanPriyanshu/AdaptKeyBERT.	['Supriti Vijay', 'Aman Priyanshu']	2022-11-14	null	null	null	null	['fact-verification', 'keyword-extraction']	['natural-language-processing', 'natural-language-processing']	[ 2.42302436e-02 9.01088677e-03 -4.77450222e-01 -3.61654222e-01 -1.10854650e+00 -6.31813705e-01 6.60932660e-01 4.44281757e-01 -5.63949883e-01 6.55776620e-01 4.08798277e-01 -4.43021089e-01 -1.04528949e-01 -8.12954485e-01 -5.92570961e-01 -2.41275787e-01 1.02329269e-01 3.98483634e-01 4.98688489e-01 -4.05692905e-01 2.65080780e-01 2.68410683e-01 -1.52298772e+00 2.18059912e-01 6.86981797e-01 8.12261462e-01 1.87130675e-01 6.50399506e-01 -5.76732755e-01 5.55355608e-01 -3.83969724e-01 -4.59908187e-01 1.87059000e-01 -1.88621491e-01 -1.02961719e+00 -4.46744889e-01 1.12077752e-02 -1.94399968e-01 -3.87158334e-01 9.84906971e-01 6.95289493e-01 4.80984032e-01 4.65062708e-01 -1.34801078e+00 -6.26350820e-01 1.01333821e+00 -2.80728906e-01 3.19181085e-01 3.70966077e-01 2.26592049e-01 1.18633163e+00 -1.12475407e+00 6.10258400e-01 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14af5b47-8075-451d-b28a-0181d94755d6	fusion-towards-automated-icd-coding-via	null	null	https://aclanthology.org/2021.findings-acl.184	https://aclanthology.org/2021.findings-acl.184.pdf	Fusion: Towards Automated ICD Coding via Feature Compression	null	['Fenglong Ma', 'Jimeng Sun', 'Lucas Glass', 'Cao Xiao', 'Junyu Luo']	null	null	null	null	findings-acl-2021-8	['feature-compression']	['computer-vision']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 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8720e5b7-4bb5-4f77-b1c1-6e24d04b8a6b	spatial-content-alignment-for-pose-transfer	2103.16828	null	https://arxiv.org/abs/2103.16828v1	https://arxiv.org/pdf/2103.16828v1.pdf	Spatial Content Alignment For Pose Transfer	Due to unreliable geometric matching and content misalignment, most conventional pose transfer algorithms fail to generate fine-trained person images. In this paper, we propose a novel framework Spatial Content Alignment GAN (SCAGAN) which aims to enhance the content consistency of garment textures and the details of human characteristics. We first alleviate the spatial misalignment by transferring the edge content to the target pose in advance. Secondly, we introduce a new Content-Style DeBlk which can progressively synthesize photo-realistic person images based on the appearance features of the source image, the target pose heatmap and the prior transferred content in edge domain. We compare the proposed framework with several state-of-the-art methods to show its superiority in quantitative and qualitative analysis. Moreover, detailed ablation study results demonstrate the efficacy of our contributions. Codes are publicly available at github.com/rocketappslab/SCA-GAN.	['Kin-Wai Lau', 'Jingjing Xiong', 'Yuzhi Zhao', 'Lai-Man Po', 'Wing-Yin Yu']	2021-03-31	null	null	null	null	['geometric-matching', 'pose-transfer']	['computer-vision', 'computer-vision']	[ 3.56615543e-01 -6.73270896e-02 2.10479721e-01 -1.37974083e-01 -7.03024805e-01 -5.80462933e-01 6.01543427e-01 -6.56954169e-01 -7.28139803e-02 9.51355338e-01 4.55732197e-01 4.21644121e-01 2.67543346e-01 -7.12611318e-01 -9.10610080e-01 -5.43430924e-01 4.94709373e-01 2.74154425e-01 -1.79713771e-01 -1.62759528e-01 -7.91204348e-02 1.67286500e-01 -1.26762700e+00 6.11903481e-02 9.45573807e-01 8.02813232e-01 4.75773513e-02 5.61859310e-01 4.77515429e-01 1.97817460e-01 -4.00045186e-01 -7.99854398e-01 4.90473002e-01 -8.94196749e-01 -5.69332778e-01 3.71710688e-01 7.63753951e-01 -5.87017298e-01 -5.66750228e-01 1.17566788e+00 7.60388494e-01 -3.88352596e-03 5.94690025e-01 -1.35208094e+00 -8.80741656e-01 6.88456744e-02 -7.23386943e-01 -2.96888590e-01 6.68704867e-01 2.24615052e-01 2.93435216e-01 -7.24643230e-01 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24d47b0f-97c3-4ad9-929e-5e842d3ad6f3	detclip-dictionary-enriched-visual-concept	2209.09407	null	https://arxiv.org/abs/2209.09407v2	https://arxiv.org/pdf/2209.09407v2.pdf	DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection	Open-world object detection, as a more general and challenging goal, aims to recognize and localize objects described by arbitrary category names. The recent work GLIP formulates this problem as a grounding problem by concatenating all category names of detection datasets into sentences, which leads to inefficient interaction between category names. This paper presents DetCLIP, a paralleled visual-concept pre-training method for open-world detection by resorting to knowledge enrichment from a designed concept dictionary. To achieve better learning efficiency, we propose a novel paralleled concept formulation that extracts concepts separately to better utilize heterogeneous datasets (i.e., detection, grounding, and image-text pairs) for training. We further design a concept dictionary~(with descriptions) from various online sources and detection datasets to provide prior knowledge for each concept. By enriching the concepts with their descriptions, we explicitly build the relationships among various concepts to facilitate the open-domain learning. The proposed concept dictionary is further used to provide sufficient negative concepts for the construction of the word-region alignment loss\, and to complete labels for objects with missing descriptions in captions of image-text pair data. The proposed framework demonstrates strong zero-shot detection performances, e.g., on the LVIS dataset, our DetCLIP-T outperforms GLIP-T by 9.9% mAP and obtains a 13.5% improvement on rare categories compared to the fully-supervised model with the same backbone as ours.	['Hang Xu', 'Chunjing Xu', 'Zhenguo Li', 'Wei zhang', 'Dan Xu', 'Xiaodan Liang', 'Youpeng Wen', 'Jianhua Han', 'Lewei Yao']	2022-09-20	null	null	null	null	['open-world-object-detection']	['computer-vision']	[ 5.52486815e-02 -8.66012648e-02 -2.79588193e-01 -2.80016512e-01 -9.20003593e-01 -7.53619075e-01 4.56493616e-01 3.43606085e-01 -5.72668910e-01 3.90498489e-01 1.67077016e-02 -7.74828047e-02 3.58314425e-01 -6.32959247e-01 -6.32546127e-01 -6.01058066e-01 3.65016133e-01 5.33607960e-01 4.62864250e-01 -4.70637828e-02 1.81590587e-01 3.65406275e-02 -1.63359261e+00 4.32334602e-01 9.23995674e-01 1.02942228e+00 7.31898904e-01 1.88851357e-01 -6.31476104e-01 4.13036585e-01 -3.76972884e-01 -5.23053288e-01 2.66309828e-01 -8.83515403e-02 -6.42030537e-01 3.36384714e-01 6.79809391e-01 -1.19823061e-01 -2.16994643e-01 1.25603342e+00 4.64328319e-01 -3.17705385e-02 7.52495348e-01 -1.15656221e+00 -8.66097867e-01 4.73920792e-01 -9.00967777e-01 3.34467798e-01 1.75368950e-01 1.47426113e-01 1.35998070e+00 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26f40678-542c-422b-956b-f15d85fd30eb	learning-emotion-aware-contextual	null	null	https://openreview.net/forum?id=msFpRstzoXt	https://openreview.net/pdf?id=msFpRstzoXt	Learning Emotion-Aware Contextual Representations for Emotion-Cause Pair Extraction	Emotion Cause Pair Extraction (ECPE), the task expanded from the previous emotion cause extraction (ECE) task, focuses on extracting emotion-cause pairs in text. Two reasons have made ECPE a more challenging, but more applicable task in real world scenarios: 1) an ECPE model needs to identify both emotions and their corresponding causes without the annotation of emotions. 2) the ECPE task involves finding causes for multiple emotions in the document context, while ECE is for one emotion. However, most existing methods for ECPE adopt an unified approach that models emotion extraction and cause extraction jointly through shared contextual representations, which is suboptimal in extracting multiple emotion-cause pairs. In addition, previous ECPE works are evaluated on one ECE dataset, which exhibits a bias that majority of documents have only one emotion-cause pair. In this work, we propose a simple pipelined approach that builds on two independent encoders, in which the emotion extraction model only provide input features for the cause extraction model. We reconstruct the benchmark dataset to better meet ECPE settings. Based on experiments conducted on the original and reconstructed dataset, we validate that our model can learn distinct contextual representations specific to each emotion, and thus achieves state-of-the-art performance on both datasets, while showing robustness in analyzing more complex document context.	['Anonymous']	2021-11-16	null	null	null	acl-arr-november-2021-11	['emotion-cause-pair-extraction', 'emotion-cause-extraction']	['natural-language-processing', 'natural-language-processing']	[ 2.97832787e-01 -7.85198063e-02 -1.31178364e-01 -3.89259785e-01 -8.95340562e-01 -7.31718600e-01 5.71739018e-01 2.28066832e-01 -2.02466235e-01 6.61080360e-01 4.77211386e-01 5.26364483e-02 -1.00453518e-01 -5.22547781e-01 -5.68052471e-01 -3.64467651e-01 4.12638262e-02 -5.74720688e-02 -3.53102714e-01 -2.19906822e-01 5.48326550e-03 1.91774413e-01 -1.85267854e+00 7.74330914e-01 6.03406072e-01 1.04248083e+00 -1.38040120e-02 7.94121265e-01 -3.34355921e-01 1.26375270e+00 -9.30476010e-01 -5.29159427e-01 -2.44008258e-01 -5.72124720e-01 -1.02760661e+00 -2.22738653e-01 5.60136735e-02 -6.62995800e-02 -4.37523127e-02 7.04164207e-01 4.39965606e-01 -2.18566917e-02 7.87849844e-01 -1.58981633e+00 -7.10651934e-01 8.35540414e-01 -3.83961856e-01 -1.51543498e-01 6.68606699e-01 -4.64019775e-01 1.48309577e+00 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3b21860f-82bd-43f1-8db4-9bb1da22a48a	hierarchical-kinematic-human-mesh-recovery	2003.04232	null	https://arxiv.org/abs/2003.04232v2	https://arxiv.org/pdf/2003.04232v2.pdf	Hierarchical Kinematic Human Mesh Recovery	We consider the problem of estimating a parametric model of 3D human mesh from a single image. While there has been substantial recent progress in this area with direct regression of model parameters, these methods only implicitly exploit the human body kinematic structure, leading to sub-optimal use of the model prior. In this work, we address this gap by proposing a new technique for regression of human parametric model that is explicitly informed by the known hierarchical structure, including joint interdependencies of the model. This results in a strong prior-informed design of the regressor architecture and an associated hierarchical optimization that is flexible to be used in conjunction with the current standard frameworks for 3D human mesh recovery. We demonstrate these aspects by means of extensive experiments on standard benchmark datasets, showing how our proposed new design outperforms several existing and popular methods, establishing new state-of-the-art results. By considering joint interdependencies, our method is equipped to infer joints even under data corruptions, which we demonstrate by conducting experiments under varying degrees of occlusion.	['Terrence Chen', 'Ren Li', 'Ziyan Wu', 'Jana Kosecka', 'Georgios Georgakis', 'Srikrishna Karanam']	2020-03-09	null	https://www.ecva.net/papers/eccv_2020/papers_ECCV/html/2889_ECCV_2020_paper.php	https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123620749.pdf	eccv-2020-8	['human-mesh-recovery']	['computer-vision']	[ 2.20057175e-01 3.62828732e-01 -1.90725163e-01 -1.16549596e-01 -5.00428915e-01 -1.76317871e-01 5.17133772e-01 -9.52636525e-02 -2.92489022e-01 5.81914127e-01 2.62561262e-01 2.49388710e-01 -2.06702486e-01 -4.79593396e-01 -8.89093935e-01 -5.19630373e-01 -2.23432794e-01 9.49336231e-01 3.33229870e-01 -3.99165392e-01 -5.47616482e-02 5.28630674e-01 -1.64123178e+00 -1.19687527e-01 4.64431167e-01 7.24604368e-01 -6.42798319e-02 5.09357274e-01 4.46803957e-01 3.79580140e-01 -3.34555596e-01 -1.93649620e-01 4.23606813e-01 -2.00988367e-01 -7.71814346e-01 5.10483623e-01 5.06930709e-01 -2.17720971e-01 -8.72331206e-03 5.71972728e-01 5.94803214e-01 -1.13333967e-02 6.31861866e-01 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4a4bf6ec-defa-47bb-aae9-023a91de69f6	neural-oscillators-are-universal	2305.08753	null	https://arxiv.org/abs/2305.08753v1	https://arxiv.org/pdf/2305.08753v1.pdf	Neural Oscillators are Universal	Coupled oscillators are being increasingly used as the basis of machine learning (ML) architectures, for instance in sequence modeling, graph representation learning and in physical neural networks that are used in analog ML devices. We introduce an abstract class of neural oscillators that encompasses these architectures and prove that neural oscillators are universal, i.e, they can approximate any continuous and casual operator mapping between time-varying functions, to desired accuracy. This universality result provides theoretical justification for the use of oscillator based ML systems. The proof builds on a fundamental result of independent interest, which shows that a combination of forced harmonic oscillators with a nonlinear read-out suffices to approximate the underlying operators.	['Siddhartha Mishra', 'T. Konstantin Rusch', 'Samuel Lanthaler']	2023-05-15	null	null	null	null	['graph-representation-learning']	['methodology']	[ 4.08188999e-01 1.82945549e-01 -2.04322502e-01 1.33222863e-01 2.27987200e-01 -7.12666094e-01 3.93456638e-01 9.22092795e-02 -2.01025277e-01 6.56061053e-01 -3.23104978e-01 -3.02899361e-01 -2.98587292e-01 -7.19585240e-01 -8.56124938e-01 -6.99307919e-01 -2.94110537e-01 1.30413160e-01 1.59475893e-01 -5.20509779e-01 1.40952632e-01 5.59167266e-01 -1.30536807e+00 -2.37554103e-01 6.24704719e-01 7.73507416e-01 -1.02537468e-01 1.03606904e+00 1.36277989e-01 9.58208680e-01 -6.81685269e-01 1.21591292e-01 -1.27123650e-02 -7.92721152e-01 -3.49136531e-01 -5.54755747e-01 1.42166480e-01 3.99908036e-01 -9.80705798e-01 1.03809929e+00 3.71848434e-01 1.72890604e-01 7.94086337e-01 -1.25016832e+00 -9.24771070e-01 1.04025447e+00 1.45768419e-01 2.47734591e-01 2.64126986e-01 -1.60346791e-01 1.00461495e+00 -4.47690010e-01 3.46717387e-01 9.18494344e-01 1.15312028e+00 6.38048351e-01 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09172cab-4eb2-48c9-ba81-56c6437f061d	lossy-compression-of-multidimensional-medical	2208.01602	null	https://arxiv.org/abs/2208.01602v2	https://arxiv.org/pdf/2208.01602v2.pdf	Lossy compression of multidimensional medical images using sinusoidal activation networks: an evaluation study	In this work, we evaluate how neural networks with periodic activation functions can be leveraged to reliably compress large multidimensional medical image datasets, with proof-of-concept application to 4D diffusion-weighted MRI (dMRI). In the medical imaging landscape, multidimensional MRI is a key area of research for developing biomarkers that are both sensitive and specific to the underlying tissue microstructure. However, the high-dimensional nature of these data poses a challenge in terms of both storage and sharing capabilities and associated costs, requiring appropriate algorithms able to represent the information in a low-dimensional space. Recent theoretical developments in deep learning have shown how periodic activation functions are a powerful tool for implicit neural representation of images and can be used for compression of 2D images. Here we extend this approach to 4D images and show how any given 4D dMRI dataset can be accurately represented through the parameters of a sinusoidal activation network, achieving a data compression rate about 10 times higher than the standard DEFLATE algorithm. Our results show that the proposed approach outperforms benchmark ReLU and Tanh activation perceptron architectures in terms of mean squared error, peak signal-to-noise ratio and structural similarity index. Subsequent analyses using the tensor and spherical harmonics representations demonstrate that the proposed lossy compression reproduces accurately the characteristics of the original data, leading to relative errors about 5 to 10 times lower than the benchmark JPEG2000 lossy compression and similar to standard pre-processing steps such as MP-PCA denosing, suggesting a loss of information within the currently accepted levels for clinical application.	['Marco Palombo', 'Derek K. Jones', 'Matteo Mancini']	2022-08-02	null	null	null	null	['data-compression']	['time-series']	[ 6.62528992e-01 -6.83815032e-03 -4.02654745e-02 -1.71403855e-01 -7.35681713e-01 -1.31639391e-01 5.15104830e-01 4.96046305e-01 -7.69744694e-01 6.28028512e-01 3.70499641e-01 -7.51272142e-02 -5.70845783e-01 -5.69975555e-01 -5.99277437e-01 -1.07487619e+00 -6.97055519e-01 5.70299745e-01 -4.06238139e-02 -3.33462581e-02 7.08554909e-02 8.74117553e-01 -1.32588255e+00 3.52377295e-01 4.42312241e-01 1.35800648e+00 2.40049109e-01 5.97724795e-01 2.88961798e-01 5.43517947e-01 -3.24952394e-01 -1.48700744e-01 3.48186702e-01 -7.27615133e-02 -8.60201955e-01 -3.12561959e-01 3.92236859e-01 -6.72808528e-01 -6.64214671e-01 1.03284299e+00 8.56915653e-01 1.00040615e-01 7.22881615e-01 -4.74549413e-01 -4.75234479e-01 3.17561358e-01 -4.86497551e-01 7.21074820e-01 -3.37115005e-02 -1.74561739e-02 4.74435538e-01 -6.30210936e-01 6.76368237e-01 8.43403459e-01 8.19705784e-01 3.29480499e-01 -1.55372512e+00 -2.93823898e-01 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4d9b03dd-cc7f-4585-ad16-0199dae15b05	hierarchical-transformer-for-survival	2211.16632	null	https://arxiv.org/abs/2211.16632v1	https://arxiv.org/pdf/2211.16632v1.pdf	Hierarchical Transformer for Survival Prediction Using Multimodality Whole Slide Images and Genomics	Learning good representation of giga-pixel level whole slide pathology images (WSI) for downstream tasks is critical. Previous studies employ multiple instance learning (MIL) to represent WSIs as bags of sampled patches because, for most occasions, only slide-level labels are available, and only a tiny region of the WSI is disease-positive area. However, WSI representation learning still remains an open problem due to: (1) patch sampling on a higher resolution may be incapable of depicting microenvironment information such as the relative position between the tumor cells and surrounding tissues, while patches at lower resolution lose the fine-grained detail; (2) extracting patches from giant WSI results in large bag size, which tremendously increases the computational cost. To solve the problems, this paper proposes a hierarchical-based multimodal transformer framework that learns a hierarchical mapping between pathology images and corresponding genes. Precisely, we randomly extract instant-level patch features from WSIs with different magnification. Then a co-attention mapping between imaging and genomics is learned to uncover the pairwise interaction and reduce the space complexity of imaging features. Such early fusion makes it computationally feasible to use MIL Transformer for the survival prediction task. Our architecture requires fewer GPU resources compared with benchmark methods while maintaining better WSI representation ability. We evaluate our approach on five cancer types from the Cancer Genome Atlas database and achieved an average c-index of $0.673$, outperforming the state-of-the-art multimodality methods.	['Junzhou Huang', 'Jiawen Yao', 'Xinliang Zhu', 'Chunyuan Li']	2022-11-29	null	null	null	null	['multiple-instance-learning']	['methodology']	[ 3.72949511e-01 3.22911404e-02 -3.48585635e-01 -1.88152432e-01 -1.54713929e+00 -2.84916788e-01 1.13869786e-01 5.89174330e-01 -2.03032140e-02 7.33705819e-01 1.28712431e-01 -4.77821603e-02 -2.10410461e-01 -8.42213273e-01 -8.83105159e-01 -1.44015348e+00 -2.11925954e-01 4.74706560e-01 1.74671367e-01 -2.60709245e-02 1.10520244e-01 3.18317682e-01 -1.15341723e+00 7.14233398e-01 6.62670195e-01 1.13727808e+00 2.78851867e-01 5.11897385e-01 -3.49544972e-01 4.51639920e-01 -3.98808151e-01 -7.44140223e-02 3.96739505e-02 -2.20709845e-01 -6.13104045e-01 -3.64612937e-02 5.70882142e-01 1.91516057e-02 -3.89950335e-01 1.06273222e+00 3.52867037e-01 -3.81403387e-01 7.14327693e-01 -9.80999947e-01 -2.58243203e-01 2.81925857e-01 -1.07859838e+00 2.36061543e-01 -4.26975675e-02 8.27708244e-02 1.13862824e+00 -4.71780509e-01 7.54883945e-01 8.55901122e-01 5.49926221e-01 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44f4a2af-e10f-4329-b8ca-5f1f4c77dff1	squeezing-large-scale-diffusion-models-for	2307.01193	null	https://arxiv.org/abs/2307.01193v1	https://arxiv.org/pdf/2307.01193v1.pdf	Squeezing Large-Scale Diffusion Models for Mobile	The emergence of diffusion models has greatly broadened the scope of high-fidelity image synthesis, resulting in notable advancements in both practical implementation and academic research. With the active adoption of the model in various real-world applications, the need for on-device deployment has grown considerably. However, deploying large diffusion models such as Stable Diffusion with more than one billion parameters to mobile devices poses distinctive challenges due to the limited computational and memory resources, which may vary according to the device. In this paper, we present the challenges and solutions for deploying Stable Diffusion on mobile devices with TensorFlow Lite framework, which supports both iOS and Android devices. The resulting Mobile Stable Diffusion achieves the inference latency of smaller than 7 seconds for a 512x512 image generation on Android devices with mobile GPUs.	['HyungJun Kim', 'Jae-Joon Kim', 'Hyesung Jeon', 'Dongwon Jo', 'Yulhwa Kim', 'Taesu Kim', 'Daehyun Ahn', 'Minkyu Kim', 'Jiwoong Choi']	2023-07-03	null	null	null	null	['image-generation']	['computer-vision']	[ 5.96015491e-02 -5.63152850e-01 -1.53091222e-01 8.54056329e-02 -2.65957445e-01 -5.45597434e-01 5.65737128e-01 -4.47890997e-01 -3.53765875e-01 5.09013116e-01 9.20272395e-02 -7.94969559e-01 -1.98508427e-02 -7.66971171e-01 -2.83200979e-01 -5.82114875e-01 -1.91120028e-01 9.08641890e-03 2.98576772e-01 2.36048903e-02 1.84736282e-01 1.62790135e-01 -1.45287752e+00 1.83484212e-01 6.59358025e-01 1.16060877e+00 3.67847502e-01 1.25638545e+00 1.37148604e-01 1.00272703e+00 -6.37998819e-01 -6.07077301e-01 2.68467039e-01 -2.17646569e-01 -4.07649010e-01 -1.03462406e-01 3.13135207e-01 -7.88216710e-01 -5.66604257e-01 1.07195818e+00 6.69583082e-01 -9.58137959e-02 1.24996364e-01 -1.27475393e+00 -8.31355512e-01 5.20694017e-01 -8.48284125e-01 7.03957140e-01 1.44005105e-01 5.64410746e-01 6.75895989e-01 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a1b5b3e9-4fe5-4f81-8e4f-2524be25dd00	knowledge-graph-embeddings-in-the-biomedical	2305.19979	null	https://arxiv.org/abs/2305.19979v1	https://arxiv.org/pdf/2305.19979v1.pdf	Knowledge Graph Embeddings in the Biomedical Domain: Are They Useful? A Look at Link Prediction, Rule Learning, and Downstream Polypharmacy Tasks	Knowledge graphs are powerful tools for representing and organising complex biomedical data. Several knowledge graph embedding algorithms have been proposed to learn from and complete knowledge graphs. However, a recent study demonstrates the limited efficacy of these embedding algorithms when applied to biomedical knowledge graphs, raising the question of whether knowledge graph embeddings have limitations in biomedical settings. This study aims to apply state-of-the-art knowledge graph embedding models in the context of a recent biomedical knowledge graph, BioKG, and evaluate their performance and potential downstream uses. We achieve a three-fold improvement in terms of performance based on the HITS@10 score over previous work on the same biomedical knowledge graph. Additionally, we provide interpretable predictions through a rule-based method. We demonstrate that knowledge graph embedding models are applicable in practice by evaluating the best-performing model on four tasks that represent real-life polypharmacy situations. Results suggest that knowledge learnt from large biomedical knowledge graphs can be transferred to such downstream use cases. Our code is available at https://github.com/aryopg/biokge.	['Ajitha Rajan', 'Antonio Vergari', 'Pasquale Minervini', 'Javier Antonio Alfaro', 'Piyush Borole', 'Wolf De Wulf', 'Dominik Grabarczyk', 'Aryo Pradipta Gema']	2023-05-31	null	null	null	null	['graph-embedding', 'link-prediction', 'knowledge-graph-embedding', 'knowledge-graph-embeddings', 'knowledge-graphs', 'knowledge-graph-embeddings']	['graphs', 'graphs', 'graphs', 'graphs', 'knowledge-base', 'methodology']	[ 1.74658984e-01 7.82787204e-01 -5.10459960e-01 -1.19319849e-01 -2.24635318e-01 -3.65740597e-01 3.16539139e-01 9.33018327e-01 -3.65729898e-01 7.57910311e-01 5.32333493e-01 -5.09166241e-01 -6.14503622e-01 -8.54473293e-01 -5.24936616e-01 -4.20122713e-01 -4.87238586e-01 6.32514775e-01 5.90527151e-03 -1.38337836e-01 -1.34500399e-01 3.91839892e-01 -8.54789615e-01 4.18776214e-01 5.12101769e-01 2.85059333e-01 -3.38762045e-01 6.33785665e-01 6.32048026e-02 7.52114475e-01 -4.92949814e-01 -5.99543214e-01 -2.44944870e-01 -2.70880312e-01 -1.11229885e+00 -5.53857088e-01 1.34095758e-01 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fa51bba6-8aee-457d-9de2-1f1640ce6555	corl-research-oriented-deep-offline	2210.07105	null	https://arxiv.org/abs/2210.07105v3	https://arxiv.org/pdf/2210.07105v3.pdf	CORL: Research-oriented Deep Offline Reinforcement Learning Library	CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms. It emphasizes a simple developing experience with a straightforward codebase and a modern analysis tracking tool. In CORL, we isolate methods implementation into separate single files, making performance-relevant details easier to recognize. Additionally, an experiment tracking feature is available to help log metrics, hyperparameters, dependencies, and more to the cloud. Finally, we have ensured the reliability of the implementations by benchmarking commonly employed D4RL datasets providing a transparent source of results that can be reused for robust evaluation tools such as performance profiles, probability of improvement, or expected online performance.	['Sergey Kolesnikov', 'Vladislav Kurenkov', 'Dmitry Akimov', 'Alexander Nikulin', 'Denis Tarasov']	2022-10-13	null	null	null	null	['d4rl']	['robots']	[-6.27624989e-01 -2.55358011e-01 -2.77817667e-01 -5.48543215e-01 -9.91872072e-01 -7.38269925e-01 5.22362471e-01 5.07960737e-01 -4.80020970e-01 8.01659107e-01 -7.82843605e-02 -5.32270133e-01 -4.34951812e-01 -3.05350333e-01 -6.60666823e-01 -5.74847162e-01 -5.74663818e-01 4.10923392e-01 2.92800784e-01 -2.70300899e-02 4.88981009e-01 5.36491334e-01 -1.84923422e+00 4.42640372e-02 2.97450840e-01 9.31238115e-01 8.11568722e-02 7.59485602e-01 2.34811977e-01 8.79766941e-01 -8.02994490e-01 -2.65252352e-01 2.99815595e-01 -1.75983027e-01 -7.19558835e-01 -5.12514234e-01 5.62181138e-02 -6.62078500e-01 -1.24077536e-01 6.98471427e-01 8.19161892e-01 -1.27394557e-01 1.38990209e-01 -1.57194424e+00 -8.74339417e-02 8.17086518e-01 -2.92908430e-01 2.74415284e-01 5.66169143e-01 8.05758178e-01 7.18535841e-01 -3.36902797e-01 6.18061662e-01 9.10994947e-01 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c5ea6dd3-93ab-4e9d-94e8-50b7fe54b649	vizinspect-pro-automated-optical-inspection	2205.13095	null	https://arxiv.org/abs/2205.13095v1	https://arxiv.org/pdf/2205.13095v1.pdf	VizInspect Pro -- Automated Optical Inspection (AOI) solution	Traditional vision based Automated Optical Inspection (referred to as AOI in paper) systems present multiple challenges in factory settings including inability to scale across multiple product lines, requirement of vendor programming expertise, little tolerance to variations and lack of cloud connectivity for aggregated insights. The lack of flexibility in these systems presents a unique opportunity for a deep learning based AOI system specifically for factory automation. The proposed solution, VizInspect pro is a generic computer vision based AOI solution built on top of Leo - An edge AI platform. Innovative features that overcome challenges of traditional vision systems include deep learning based image analysis which combines the power of self-learning with high speed and accuracy, an intuitive user interface to configure inspection profiles in minutes without ML or vision expertise and the ability to solve complex inspection challenges while being tolerant to deviations and unpredictable defects. This solution has been validated by multiple external enterprise customers with confirmed value propositions. In this paper we show you how this solution and platform solved problems around model development, deployment, scaling multiple inferences and visualizations.	['Debashis Mondal', 'Haotian Xu', 'Sanjit Menon', 'Faraz Waseem']	2022-05-26	null	null	null	null	['self-learning']	['natural-language-processing']	[-4.58545417e-01 -3.76768969e-02 9.95281279e-01 -2.35575318e-01 1.10908985e-01 -7.42500961e-01 -3.02641630e-01 2.00286344e-01 5.19108176e-01 -7.01499805e-02 -6.02784514e-01 -4.03521746e-01 -1.02762365e+00 -4.95863765e-01 -2.82499939e-01 -4.03211832e-01 -1.02999806e-02 6.82508409e-01 7.14156553e-02 -5.19806445e-01 2.04806045e-01 7.15465426e-01 -1.57541823e+00 3.29817325e-01 8.78474593e-01 1.23806143e+00 4.52318490e-01 9.27739859e-01 2.14119088e-02 8.33005250e-01 -9.73122001e-01 3.79718512e-01 5.38070142e-01 2.23321781e-01 -3.76874298e-01 3.79962355e-01 4.40298706e-01 -2.68555105e-01 3.20193559e-01 5.50428152e-01 4.67622519e-01 -4.20780063e-01 6.08808920e-02 -1.69141829e+00 -1.18396246e+00 -1.53519556e-01 -4.57203746e-01 2.47782394e-01 2.97458082e-01 8.02891970e-01 5.54860532e-01 -5.86167634e-01 3.64905328e-01 9.40182447e-01 1.20085168e+00 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12afad55-e9de-4ae6-9445-ba0a80555dfa	the-limitations-of-cross-language-word	1806.02253	null	http://arxiv.org/abs/1806.02253v1	http://arxiv.org/pdf/1806.02253v1.pdf	The Limitations of Cross-language Word Embeddings Evaluation	The aim of this work is to explore the possible limitations of existing methods of cross-language word embeddings evaluation, addressing the lack of correlation between intrinsic and extrinsic cross-language evaluation methods. To prove this hypothesis, we construct English-Russian datasets for extrinsic and intrinsic evaluation tasks and compare performances of 5 different cross-language models on them. The results say that the scores even on different intrinsic benchmarks do not correlate to each other. We can conclude that the use of human references as ground truth for cross-language word embeddings is not proper unless one does not understand how do native speakers process semantics in their cognition.	['Roman Suvorov', 'Ilya Sochenkov', 'Amir Bakarov']	2018-06-06	the-limitations-of-cross-language-word-1	https://aclanthology.org/S18-2010	https://aclanthology.org/S18-2010.pdf	semeval-2018-6	['embeddings-evaluation']	['natural-language-processing']	[-3.75980943e-01 6.45571873e-02 -2.73206264e-01 -5.17087042e-01 -3.50176364e-01 -6.52949810e-01 1.00206792e+00 6.47592783e-01 -1.25429654e+00 6.69110537e-01 5.52168310e-01 -3.35220397e-01 -1.59324229e-01 -7.07689822e-01 -2.68494338e-01 -6.39880672e-02 5.69127262e-01 5.49139023e-01 2.62286067e-01 -5.58111608e-01 3.41698557e-01 1.79485261e-01 -1.35636044e+00 5.91882057e-02 1.01510084e+00 1.78958610e-01 -1.20074362e-01 5.08335352e-01 -3.71198833e-01 4.04740125e-01 -7.05628157e-01 -9.85222697e-01 -8.06150436e-02 -3.58395338e-01 -9.05294418e-01 -5.66990793e-01 5.48205316e-01 9.32939574e-02 -9.61727574e-02 1.28108883e+00 3.55086088e-01 6.61060661e-02 9.78962302e-01 -8.69003236e-01 -1.43296254e+00 9.85513449e-01 6.55273115e-03 4.43391889e-01 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9fef98d8-76c2-468e-b002-5355cab3a649	heart-rate-variability-as-a-predictive	2005.08031	null	https://arxiv.org/abs/2005.08031v3	https://arxiv.org/pdf/2005.08031v3.pdf	Heart Rate Variability as a Predictive Biomarker of Thermal Comfort	Thermal comfort is an assessment of one's satisfaction with the surroundings; yet, most mechanisms that are used to provide thermal comfort are based on approaches that preclude physiological, psychological, and personal psychophysics that are precursors to thermal comfort. This leads to many people feeling either cold or hot in an environment that was supposed to be thermally comfortable to most users. To address this problem, this paper proposes to use heart rate variability (HRV) as an alternative indicator of thermal comfort status. Since HRV is linked to homeostasis, we conjectured that people's thermal comfort could be more accurately estimated based on their heart rate variability (HRV). To test our hypothesis, we analyzed statistical, spectral, and nonlinear HRV indices of 17 human subjects doing light office work in a cold, neutral, and hot environment. The resulting HRV indices were used as inputs to machine learning classification algorithms. We observed that HRV is distinctively different depending on the thermal environment and that it is possible to reliably predict each subject's thermal state (cold, neutral, and hot) with up to 93.7% accuracy. The result of this study suggests that it could be possible to design automatic real-time thermal comfort controllers based on people's HRV.	['Guillaume Lopez', 'Yuta Suzuki', 'Kizito Nkurikiyeyezu']	2020-05-16	null	null	null	null	['heart-rate-variability']	['medical']	[-7.76761919e-02 -1.14899419e-01 1.78644862e-02 -4.88873184e-01 2.70451158e-01 -3.91604900e-01 1.10449456e-01 2.21142352e-01 -3.13561141e-01 7.80629516e-01 8.22202116e-02 -1.91555038e-01 2.17228860e-01 -6.58347905e-01 2.79640734e-01 -6.67735457e-01 1.05443045e-01 -1.65207714e-01 -4.72738594e-01 -3.14741611e-01 2.54283577e-01 4.58013624e-01 -1.61499465e+00 -3.94260436e-02 8.25793207e-01 9.47044909e-01 -4.31360081e-02 7.69564867e-01 4.68824774e-01 3.71646918e-02 -7.05571711e-01 8.04993808e-02 1.06230460e-01 -6.38985813e-01 -4.74770546e-01 -4.08852637e-01 -1.51432410e-01 -5.60050830e-02 2.72053659e-01 3.42200786e-01 5.75959027e-01 6.65642917e-01 6.25328839e-01 -1.20417154e+00 -4.75426316e-01 -2.23325476e-01 1.90400317e-01 8.67758989e-02 6.28306031e-01 4.28445429e-01 2.80784518e-01 -6.43116683e-02 -7.07760174e-03 7.52410293e-01 5.86451709e-01 5.46653450e-01 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-1.00127363e+00 -8.57609332e-01 2.18123928e-01 2.84842014e-01 -2.95266718e-01 -6.13098860e-01 -1.46641266e-02]	[13.74846076965332, 3.021015167236328]
8f434662-0b67-4dbd-9b4a-baf4dde9b68e	learning-state-aware-visual-representations	2209.13583	null	https://arxiv.org/abs/2209.13583v1	https://arxiv.org/pdf/2209.13583v1.pdf	Learning State-Aware Visual Representations from Audible Interactions	We propose a self-supervised algorithm to learn representations from egocentric video data. Recently, significant efforts have been made to capture humans interacting with their own environments as they go about their daily activities. In result, several large egocentric datasets of interaction-rich multi-modal data have emerged. However, learning representations from videos can be challenging. First, given the uncurated nature of long-form continuous videos, learning effective representations require focusing on moments in time when interactions take place. Second, visual representations of daily activities should be sensitive to changes in the state of the environment. However, current successful multi-modal learning frameworks encourage representation invariance over time. To address these challenges, we leverage audio signals to identify moments of likely interactions which are conducive to better learning. We also propose a novel self-supervised objective that learns from audible state changes caused by interactions. We validate these contributions extensively on two large-scale egocentric datasets, EPIC-Kitchens-100 and the recently released Ego4D, and show improvements on several downstream tasks, including action recognition, long-term action anticipation, and object state change classification.	['Abhinav Gupta', 'Unnat Jain', 'Pedro Morgado', 'Himangi Mittal']	2022-09-27	null	null	null	null	['action-anticipation']	['computer-vision']	[ 3.15584809e-01 -2.42161781e-01 -2.49966979e-01 -5.58184266e-01 -3.84566635e-01 -3.60811949e-01 6.57827437e-01 4.86936197e-02 -1.51495457e-01 4.78336722e-01 1.08215487e+00 7.41318524e-01 -1.54010952e-01 -5.50187290e-01 -9.26910639e-01 -2.97185570e-01 -4.94349182e-01 2.14158073e-01 3.12951319e-02 -2.74915516e-01 9.40081328e-02 3.50877792e-01 -1.92870879e+00 6.51654661e-01 2.11157680e-01 7.40963578e-01 4.90591116e-02 9.39853489e-01 6.29430652e-01 1.13829136e+00 -2.31837273e-01 3.69719267e-01 8.57620761e-02 -4.55698997e-01 -6.46284699e-01 3.53699386e-01 4.59825128e-01 -5.53137362e-01 -1.04624856e+00 5.75310767e-01 6.22771084e-01 7.67079473e-01 6.19211376e-01 -1.24924111e+00 -3.08552444e-01 4.10535753e-01 -2.88170725e-01 5.61553478e-01 8.81758749e-01 4.38188881e-01 8.49678218e-01 -3.90636116e-01 7.95713186e-01 1.31494594e+00 3.90337616e-01 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7a794f08-bd3c-49cb-b437-56bcb232dbd3	deep-learning-inversion-a-next-generation	1902.06267	null	http://arxiv.org/abs/1902.06267v1	http://arxiv.org/pdf/1902.06267v1.pdf	Deep-learning inversion: a next generation seismic velocity-model building method	Seismic velocity is one of the most important parameters used in seismic exploration. Accurate velocity models are key prerequisites for reverse-time migration and other high-resolution seismic imaging techniques. Such velocity information has traditionally been derived by tomography or full-waveform inversion (FWI), which are time consuming and computationally expensive, and they rely heavily on human interaction and quality control. We investigate a novel method based on the supervised deep fully convolutional neural network (FCN) for velocity-model building (VMB) directly from raw seismograms. Unlike the conventional inversion method based on physical models, the supervised deep-learning methods are based on big-data training rather than prior-knowledge assumptions. During the training stage, the network establishes a nonlinear projection from the multi-shot seismic data to the corresponding velocity models. During the prediction stage, the trained network can be used to estimate the velocity models from the new input seismic data. One key characteristic of the deep-learning method is that it can automatically extract multi-layer useful features without the need for human-curated activities and initial velocity setup. The data-driven method usually requires more time during the training stage, and actual predictions take less time, with only seconds needed. Therefore, the computational time of geophysical inversions, including real-time inversions, can be dramatically reduced once a good generalized network is built. By using numerical experiments on synthetic models, the promising performances of our proposed method are shown in comparison with conventional FWI even when the input data are in more realistic scenarios. Discussions on the deep-learning methods, training dataset, lack of low frequencies, and advantages and disadvantages of the new method are also provided.	['Jianwei Ma', 'Fangshu Yang']	2019-02-17	null	null	null	null	['seismic-imaging']	['miscellaneous']	[ 3.96238975e-02 -1.31524652e-01 2.55132586e-01 -1.66288212e-01 -8.57908607e-01 -3.39798741e-02 4.35833216e-01 -3.02818239e-01 -6.24745846e-01 7.02290177e-01 -1.39459401e-01 -4.49851036e-01 -4.26672429e-01 -1.10678458e+00 -7.40276158e-01 -1.13228011e+00 -2.74619460e-01 6.01092398e-01 3.87264341e-01 -1.73229620e-01 4.20593202e-01 5.48063219e-01 -1.20237792e+00 -2.82615781e-01 9.32718635e-01 1.11468792e+00 3.55059236e-01 4.54854190e-01 -1.50220320e-01 6.57236457e-01 -2.67862827e-01 2.69225657e-01 -1.85589888e-03 -3.49023879e-01 -7.33154655e-01 -1.65261731e-01 -4.65918124e-01 -5.88971078e-01 -3.49497944e-01 7.70870566e-01 5.93633771e-01 3.20258230e-01 7.60440707e-01 -9.01256084e-01 8.64328593e-02 5.64664781e-01 -6.29369199e-01 5.64005934e-02 -7.40755424e-02 3.25720608e-02 5.33718526e-01 -1.10997188e+00 2.71453530e-01 7.14489520e-01 9.86923695e-01 1.59448236e-01 -1.10363483e+00 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2.21219018e-01 4.21128690e-01 -8.52476954e-01 -5.30618668e-01 1.53033867e-01]	[6.853077411651611, 2.547147750854492]
92c2b9cc-579d-4370-8a5e-7968e3ea2358	improving-universal-sound-separation-using	1911.07951	null	https://arxiv.org/abs/1911.07951v1	https://arxiv.org/pdf/1911.07951v1.pdf	Improving Universal Sound Separation Using Sound Classification	Deep learning approaches have recently achieved impressive performance on both audio source separation and sound classification. Most audio source separation approaches focus only on separating sources belonging to a restricted domain of source classes, such as speech and music. However, recent work has demonstrated the possibility of "universal sound separation", which aims to separate acoustic sources from an open domain, regardless of their class. In this paper, we utilize the semantic information learned by sound classifier networks trained on a vast amount of diverse sounds to improve universal sound separation. In particular, we show that semantic embeddings extracted from a sound classifier can be used to condition a separation network, providing it with useful additional information. This approach is especially useful in an iterative setup, where source estimates from an initial separation stage and their corresponding classifier-derived embeddings are fed to a second separation network. By performing a thorough hyperparameter search consisting of over a thousand experiments, we find that classifier embeddings from clean sources provide nearly one dB of SNR gain, and our best iterative models achieve a significant fraction of this oracle performance, establishing a new state-of-the-art for universal sound separation.	['Scott Wisdom', 'Efthymios Tzinis', 'John R. Hershey', 'Daniel P. W. Ellis', 'Aren Jansen']	2019-11-18	null	null	null	null	['audio-source-separation', 'sound-classification']	['audio', 'audio']	[ 3.36756855e-01 -2.85502285e-01 1.12158405e-02 -5.95128462e-02 -1.46614897e+00 -1.00024641e+00 2.25408003e-01 1.58082813e-01 -6.84238225e-02 4.66098368e-01 4.23260421e-01 -1.24921165e-01 -2.63681114e-01 -2.66452044e-01 -4.28608388e-01 -8.99314761e-01 -1.32284328e-01 2.16254115e-01 1.21766806e-01 5.15321568e-02 -7.66345635e-02 4.27206904e-01 -1.67453074e+00 6.69071227e-02 6.58292770e-01 1.10001731e+00 3.37874927e-02 1.07319939e+00 -1.66181937e-01 4.12328571e-01 -8.44541967e-01 -1.13138005e-01 3.12713653e-01 -7.48689055e-01 -6.48001373e-01 -4.70304489e-01 4.20542151e-01 -1.62272647e-01 -2.75783449e-01 1.13124108e+00 9.88950074e-01 3.13083500e-01 7.04310060e-01 -1.19440675e+00 -3.55929285e-01 9.91369247e-01 -6.54027015e-02 4.14655149e-01 2.78914899e-01 -2.27842897e-01 1.44561327e+00 -8.58555913e-01 -9.43000913e-02 9.08225477e-01 7.66589820e-01 5.26214957e-01 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-8.89455020e-01 1.82534248e-01 1.09844637e+00 -5.40684581e-01 -1.04641795e+00 1.53968081e-01]	[15.357918739318848, 5.47518253326416]
8dfcb46e-e2ed-4192-bc7d-66d3a2d1c73c	a-novel-dataset-and-a-two-stage-mitosis	2301.07627	null	https://arxiv.org/abs/2301.07627v1	https://arxiv.org/pdf/2301.07627v1.pdf	A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch	Mitosis detection is one of the challenging problems in computational pathology, and mitotic count is an important index of cancer grading for pathologists. However, current counts of mitotic nuclei rely on pathologists looking microscopically at the number of mitotic nuclei in hot spots, which is subjective and time-consuming. In this paper, we propose a two-stage cascaded network, named FoCasNet, for mitosis detection. In the first stage, a detection network named M_det is proposed to detect as many mitoses as possible. In the second stage, a classification network M_class is proposed to refine the results of the first stage. In addition, the attention mechanism, normalization method, and hybrid anchor branch classification subnet are introduced to improve the overall detection performance. Our method achieves the current highest F1-score of 0.888 on the public dataset ICPR 2012. We also evaluated our method on the GZMH dataset released by our research team for the first time and reached the highest F1-score of 0.563, which is also better than multiple classic detection networks widely used at present. It confirmed the effectiveness and generalization of our method. The code will be available at: https://github.com/antifen/mitosis-nuclei-detection.	['Xiaonan Luo', 'Rushi Lan', 'Lingqi Zeng', 'Xipeng Pan', 'Bingbing Li', 'Hao Xu', 'Huadeng Wang']	2023-01-18	null	null	null	null	['mitosis-detection']	['medical']	[-6.78843586e-03 1.15038320e-01 -4.33506489e-01 7.02124387e-02 -8.74984264e-01 -4.53192174e-01 2.80066550e-01 5.43028593e-01 -6.47567809e-01 7.54405081e-01 -6.72949702e-02 -3.79362911e-01 1.67398855e-01 -9.06793177e-01 -1.75448254e-01 -1.17473221e+00 3.31137300e-01 3.23879391e-01 5.33924162e-01 3.61007266e-02 3.28567088e-01 4.72015440e-01 -1.08768034e+00 6.46169707e-02 7.42008448e-01 7.20769882e-01 2.43530512e-01 8.49805832e-01 2.97998786e-02 5.15728533e-01 -4.23847467e-01 -3.53271544e-01 -1.85202897e-01 -2.93670893e-01 -6.73077583e-01 -1.17954426e-01 -3.62209603e-02 -1.35336429e-01 -3.32018733e-01 1.33338845e+00 5.55899799e-01 -3.40765774e-01 7.93901086e-01 -9.74589646e-01 -2.31788561e-01 7.87311018e-01 -9.64582920e-01 5.49282551e-01 -1.31298646e-01 3.82603109e-01 1.04429364e+00 -7.38377392e-01 8.11989546e-01 5.51520169e-01 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-5.25926888e-01 -2.04366937e-01 -1.17745447e+00 -6.93007529e-01 7.09818378e-02 2.86930710e-01 -4.53322679e-01 -6.43708766e-01 1.09698018e-02]	[15.053014755249023, -3.0807580947875977]
a696bae4-1871-4ae6-9b08-c9b50fdf1888	action-conditioned-3d-human-motion-synthesis	2104.05670	null	https://arxiv.org/abs/2104.05670v2	https://arxiv.org/pdf/2104.05670v2.pdf	Action-Conditioned 3D Human Motion Synthesis with Transformer VAE	We tackle the problem of action-conditioned generation of realistic and diverse human motion sequences. In contrast to methods that complete, or extend, motion sequences, this task does not require an initial pose or sequence. Here we learn an action-aware latent representation for human motions by training a generative variational autoencoder (VAE). By sampling from this latent space and querying a certain duration through a series of positional encodings, we synthesize variable-length motion sequences conditioned on a categorical action. Specifically, we design a Transformer-based architecture, ACTOR, for encoding and decoding a sequence of parametric SMPL human body models estimated from action recognition datasets. We evaluate our approach on the NTU RGB+D, HumanAct12 and UESTC datasets and show improvements over the state of the art. Furthermore, we present two use cases: improving action recognition through adding our synthesized data to training, and motion denoising. Code and models are available on our project page.	['Gül Varol', 'Michael J. Black', 'Mathis Petrovich']	2021-04-12	null	http://openaccess.thecvf.com//content/ICCV2021/html/Petrovich_Action-Conditioned_3D_Human_Motion_Synthesis_With_Transformer_VAE_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Petrovich_Action-Conditioned_3D_Human_Motion_Synthesis_With_Transformer_VAE_ICCV_2021_paper.pdf	iccv-2021-1	['human-action-generation']	['computer-vision']	[ 5.32690227e-01 1.46825835e-01 -1.21182665e-01 -2.98816651e-01 -8.81673276e-01 -4.98427093e-01 8.11160207e-01 -1.09715235e+00 -2.62936860e-01 6.81456447e-01 8.04214716e-01 1.98855445e-01 3.36081386e-01 -5.46551943e-01 -1.00981987e+00 -8.41663301e-01 1.80578470e-01 7.83545792e-01 1.49799973e-01 -4.30493057e-02 -1.34130239e-01 3.59690964e-01 -1.60741901e+00 6.69855177e-01 2.82049477e-01 7.82170892e-01 2.56356508e-01 1.24767315e+00 2.95890540e-01 1.15859747e+00 -5.44113576e-01 -1.74464822e-01 1.82007939e-01 -9.91784871e-01 -9.43773866e-01 5.81889689e-01 3.95918369e-01 -7.70903826e-01 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d81af21e-531b-40a2-b814-d5787c2488b2	schnet-a-continuous-filter-convolutional	1706.08566	null	http://arxiv.org/abs/1706.08566v5	http://arxiv.org/pdf/1706.08566v5.pdf	SchNet: A continuous-filter convolutional neural network for modeling quantum interactions	Deep learning has the potential to revolutionize quantum chemistry as it is ideally suited to learn representations for structured data and speed up the exploration of chemical space. While convolutional neural networks have proven to be the first choice for images, audio and video data, the atoms in molecules are not restricted to a grid. Instead, their precise locations contain essential physical information, that would get lost if discretized. Thus, we propose to use continuous-filter convolutional layers to be able to model local correlations without requiring the data to lie on a grid. We apply those layers in SchNet: a novel deep learning architecture modeling quantum interactions in molecules. We obtain a joint model for the total energy and interatomic forces that follows fundamental quantum-chemical principles. This includes rotationally invariant energy predictions and a smooth, differentiable potential energy surface. Our architecture achieves state-of-the-art performance for benchmarks of equilibrium molecules and molecular dynamics trajectories. Finally, we introduce a more challenging benchmark with chemical and structural variations that suggests the path for further work.	['Klaus-Robert Müller', 'Pieter-Jan Kindermans', 'Kristof T. Schütt', 'Huziel E. Sauceda', 'Alexandre Tkatchenko', 'Stefan Chmiela']	2017-06-26	schnet-a-continuous-filter-convolutional-1	http://papers.nips.cc/paper/6700-schnet-a-continuous-filter-convolutional-neural-network-for-modeling-quantum-interactions	http://papers.nips.cc/paper/6700-schnet-a-continuous-filter-convolutional-neural-network-for-modeling-quantum-interactions.pdf	neurips-2017-12	['formation-energy']	['miscellaneous']	[ 3.16858031e-02 -2.77545750e-01 -1.72889456e-01 -4.80373114e-01 -5.85193217e-01 -6.31392241e-01 7.91435897e-01 5.23912072e-01 -5.63914239e-01 9.75855529e-01 -5.63106872e-02 -5.81967771e-01 -2.61387751e-02 -1.02368426e+00 -1.10629463e+00 -1.00100315e+00 -5.99675894e-01 4.87935632e-01 -1.17906690e-01 -2.82762170e-01 3.77583653e-01 9.01423097e-01 -1.16819942e+00 4.76232737e-01 7.48704612e-01 7.85159767e-01 -1.33158013e-01 5.89773595e-01 7.33056664e-02 7.02896237e-01 -2.94434756e-01 -1.43944383e-01 8.81441161e-02 -4.99352217e-01 -9.56717074e-01 -6.33407235e-01 5.07418513e-01 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93a93fac-6eb1-4234-9abe-e95de1bce438	offline-reinforcement-learning-with-value-1	2110.09796	null	https://arxiv.org/abs/2110.09796v1	https://arxiv.org/pdf/2110.09796v1.pdf	Offline Reinforcement Learning with Value-based Episodic Memory	Offline reinforcement learning (RL) shows promise of applying RL to real-world problems by effectively utilizing previously collected data. Most existing offline RL algorithms use regularization or constraints to suppress extrapolation error for actions outside the dataset. In this paper, we adopt a different framework, which learns the V-function instead of the Q-function to naturally keep the learning procedure within the support of an offline dataset. To enable effective generalization while maintaining proper conservatism in offline learning, we propose Expectile V-Learning (EVL), which smoothly interpolates between the optimal value learning and behavior cloning. Further, we introduce implicit planning along offline trajectories to enhance learned V-values and accelerate convergence. Together, we present a new offline method called Value-based Episodic Memory (VEM). We provide theoretical analysis for the convergence properties of our proposed VEM method, and empirical results in the D4RL benchmark show that our method achieves superior performance in most tasks, particularly in sparse-reward tasks.	['Bin Liang', 'Qianchuan Zhao', 'Chongjie Zhang', 'Jun Yang', 'Qihan Liu', 'Hao Hu', 'Yiqin Yang', 'Xiaoteng Ma']	2021-10-19	offline-reinforcement-learning-with-value	https://openreview.net/forum?id=RCZqv9NXlZ	https://openreview.net/pdf?id=RCZqv9NXlZ	iclr-2022-4	['d4rl']	['robots']	[-2.18807146e-01 2.16681600e-01 -7.41528928e-01 -2.65028000e-01 -8.63463581e-01 -4.99981165e-01 3.30192983e-01 1.07732967e-01 -6.61531389e-01 1.18019974e+00 2.14714766e-01 -2.07579032e-01 -4.30088520e-01 -5.49838841e-01 -1.01467264e+00 -7.24228203e-01 -5.41845024e-01 1.77844584e-01 3.69544467e-03 -1.46659836e-01 3.80481541e-01 3.13662976e-01 -1.10307193e+00 -8.09022188e-02 1.13208365e+00 1.07244968e+00 4.31736559e-01 1.84556454e-01 1.51611984e-01 1.22842968e+00 -3.09569985e-01 4.33922969e-02 7.27551222e-01 -5.29243648e-01 -7.36394942e-01 9.79626179e-02 -1.96197331e-01 -7.21631110e-01 -7.77950466e-01 8.21256697e-01 4.11603034e-01 8.04373145e-01 1.90517694e-01 -1.21144807e+00 -4.97443736e-01 8.39406431e-01 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