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2303.15671
Suncheng Xiang
Qingzhong Chen, Shilun Cai, Crystal Cai, Zefang Yu, Dahong Qian, Suncheng Xiang
Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval
Accepted by ICME 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Colonoscopic video retrieval, which is a critical part of polyp treatment, has great clinical significance for the prevention and treatment of colorectal cancer. However, retrieval models trained on action recognition datasets usually produce unsatisfactory retrieval results on colonoscopic datasets due to the large domain gap between them. To seek a solution to this problem, we construct a large-scale colonoscopic dataset named Colo-Pair for medical practice. Based on this dataset, a simple yet effective training method called Colo-SCRL is proposed for more robust representation learning. It aims to refine general knowledge from colonoscopies through masked autoencoder-based reconstruction and momentum contrast to improve retrieval performance. To the best of our knowledge, this is the first attempt to employ the contrastive learning paradigm for medical video retrieval. Empirical results show that our method significantly outperforms current state-of-the-art methods in the colonoscopic video retrieval task.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 01:27:23 GMT" } ]
2023-03-29T00:00:00
[ [ "Chen", "Qingzhong", "" ], [ "Cai", "Shilun", "" ], [ "Cai", "Crystal", "" ], [ "Yu", "Zefang", "" ], [ "Qian", "Dahong", "" ], [ "Xiang", "Suncheng", "" ] ]
new_dataset
0.997412
2303.15732
Farhan Rozaidi
Farhan Rozaidi, Emma Waters, Olivia Dawes, Jennifer Yang, Joseph R. Davidson, Ross L. Hatton
HISSbot: Sidewinding with a Soft Snake Robot
7 pages, 9 figures, to be published in RoboSoft 2023
null
null
null
cs.RO
http://creativecommons.org/licenses/by/4.0/
Snake robots are characterized by their ability to navigate through small spaces and loose terrain by utilizing efficient cyclic forms of locomotion. Soft snake robots are a subset of these robots which utilize soft, compliant actuators to produce movement. Prior work on soft snake robots has primarily focused on planar gaits, such as undulation. More efficient spatial gaits, such as sidewinding, are unexplored gaits for soft snake robots. We propose a novel means of constructing a soft snake robot capable of sidewinding, and introduce the Helical Inflating Soft Snake Robot (HISSbot). We validate this actuation through the physical HISSbot, and demonstrate its ability to sidewind across various surfaces. Our tests show robustness in locomotion through low-friction and granular media.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 04:57:30 GMT" } ]
2023-03-29T00:00:00
[ [ "Rozaidi", "Farhan", "" ], [ "Waters", "Emma", "" ], [ "Dawes", "Olivia", "" ], [ "Yang", "Jennifer", "" ], [ "Davidson", "Joseph R.", "" ], [ "Hatton", "Ross L.", "" ] ]
new_dataset
0.999207
2303.15762
Shlomi Steinberg
Shlomi Steinberg, Ravi Ramamoorthi, Benedikt Bitterli, Eugene d'Eon, Ling-Qi Yan, Matt Pharr
A Generalized Ray Formulation For Wave-Optics Rendering
For additional information, see https://ssteinberg.xyz/2023/03/27/rtplt/
null
null
null
cs.GR
http://creativecommons.org/licenses/by-nc-sa/4.0/
Under ray-optical light transport, the classical ray serves as a local and linear "point query" of light's behaviour. Such point queries are useful, and sophisticated path tracing and sampling techniques enable efficiently computing solutions to light transport problems in complex, real-world settings and environments. However, such formulations are firmly confined to the realm of ray optics, while many applications of interest, in computer graphics and computational optics, demand a more precise understanding of light. We rigorously formulate the generalized ray, which enables local and linear point queries of the wave-optical phase space. Furthermore, we present sample-solve: a simple method that serves as a novel link between path tracing and computational optics. We will show that this link enables the application of modern path tracing techniques for wave-optical rendering, improving upon the state-of-the-art in terms of the generality and accuracy of the formalism, ease of application, as well as performance. Sampling using generalized rays enables interactive rendering under rigorous wave optics, with orders-of-magnitude faster performance compared to existing techniques.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 06:42:52 GMT" } ]
2023-03-29T00:00:00
[ [ "Steinberg", "Shlomi", "" ], [ "Ramamoorthi", "Ravi", "" ], [ "Bitterli", "Benedikt", "" ], [ "d'Eon", "Eugene", "" ], [ "Yan", "Ling-Qi", "" ], [ "Pharr", "Matt", "" ] ]
new_dataset
0.991365
2303.15780
Hiromichi Kamata
Hiromichi Kamata, Yuiko Sakuma, Akio Hayakawa, Masato Ishii, Takuya Narihira
Instruct 3D-to-3D: Text Instruction Guided 3D-to-3D conversion
Project page: https://sony.github.io/Instruct3Dto3D-doc/
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
We propose a high-quality 3D-to-3D conversion method, Instruct 3D-to-3D. Our method is designed for a novel task, which is to convert a given 3D scene to another scene according to text instructions. Instruct 3D-to-3D applies pretrained Image-to-Image diffusion models for 3D-to-3D conversion. This enables the likelihood maximization of each viewpoint image and high-quality 3D generation. In addition, our proposed method explicitly inputs the source 3D scene as a condition, which enhances 3D consistency and controllability of how much of the source 3D scene structure is reflected. We also propose dynamic scaling, which allows the intensity of the geometry transformation to be adjusted. We performed quantitative and qualitative evaluations and showed that our proposed method achieves higher quality 3D-to-3D conversions than baseline methods.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 07:50:45 GMT" } ]
2023-03-29T00:00:00
[ [ "Kamata", "Hiromichi", "" ], [ "Sakuma", "Yuiko", "" ], [ "Hayakawa", "Akio", "" ], [ "Ishii", "Masato", "" ], [ "Narihira", "Takuya", "" ] ]
new_dataset
0.998665
2303.15782
Nick Heppert
Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu, Rares Andrei Ambrus, Jeannette Bohg, Abhinav Valada, Thomas Kollar
CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects
20 pages, 11 figures, accepted at CVPR 2023
null
null
null
cs.CV cs.RO
http://creativecommons.org/licenses/by/4.0/
We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation. We use implicit object-centric representations and learn a single geometry and articulation decoder for multiple object categories. Despite training on multiple categories, our decoder achieves a comparable reconstruction accuracy to methods that train bespoke decoders separately for each category. Combined with our stereo image encoder we infer the 3D shape, 6D pose, size, joint type, and the joint state of multiple unknown objects in a single forward pass. Our method achieves a 20.4% absolute improvement in mAP 3D IOU50 for novel instances when compared to a two-stage pipeline. Inference time is fast and can run on a NVIDIA TITAN XP GPU at 1 HZ for eight or less objects present. While only trained on simulated data, CARTO transfers to real-world object instances. Code and evaluation data is available at: http://carto.cs.uni-freiburg.de
[ { "version": "v1", "created": "Tue, 28 Mar 2023 07:52:15 GMT" } ]
2023-03-29T00:00:00
[ [ "Heppert", "Nick", "" ], [ "Irshad", "Muhammad Zubair", "" ], [ "Zakharov", "Sergey", "" ], [ "Liu", "Katherine", "" ], [ "Ambrus", "Rares Andrei", "" ], [ "Bohg", "Jeannette", "" ], [ "Valada", "Abhinav", "" ], [ "Kollar", "Thomas", "" ] ]
new_dataset
0.991689
2303.15784
EPTCS
Stephen Mell, Osbert Bastani, Steve Zdancewic
Ideograph: A Language for Expressing and Manipulating Structured Data
In Proceedings TERMGRAPH 2022, arXiv:2303.14219
EPTCS 377, 2023, pp. 65-84
10.4204/EPTCS.377.4
null
cs.PL
http://creativecommons.org/licenses/by/4.0/
We introduce Ideograph, a language for expressing and manipulating structured data. Its types describe kinds of structures, such as natural numbers, lists, multisets, binary trees, syntax trees with variable binding, directed multigraphs, and relational databases. Fully normalized terms of a type correspond exactly to members of the structure, analogous to a Church-encoding. Moreover, definable operations over these structures are guaranteed to respect the structures' equivalences. In this paper, we give the syntax and semantics of the non-polymorphic subset of Ideograph, and we demonstrate how it can represent and manipulate several interesting structures.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 07:52:50 GMT" } ]
2023-03-29T00:00:00
[ [ "Mell", "Stephen", "" ], [ "Bastani", "Osbert", "" ], [ "Zdancewic", "Steve", "" ] ]
new_dataset
0.990679
2303.15819
Monika Dalal
Monika Dalal, Sucheta Dutt and Ranjeet Sehmi
MDS and MHDR cyclic codes over finite chain rings
null
null
null
null
cs.IT math.IT
http://creativecommons.org/licenses/by-nc-sa/4.0/
In this work, a unique set of generators for a cyclic code over a finite chain ring has been established. The minimal spanning set and rank of the code have also been determined. Further, sufficient as well as necessary conditions for a cyclic code to be an MDS code and for a cyclic code to be an MHDR code have been obtained. Some examples of optimal cyclic codes have also been presented.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 08:45:14 GMT" } ]
2023-03-29T00:00:00
[ [ "Dalal", "Monika", "" ], [ "Dutt", "Sucheta", "" ], [ "Sehmi", "Ranjeet", "" ] ]
new_dataset
0.999283
2303.15822
Deze Wang
Deze Wang, Boxing Chen, Shanshan Li, Wei Luo, Shaoliang Peng, Wei Dong, Xiangke Liao
One Adapter for All Programming Languages? Adapter Tuning for Code Search and Summarization
Accepted to the 45th International Conference on Software Engineering (ICSE 2023)
null
null
null
cs.SE cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
As pre-trained models automate many code intelligence tasks, a widely used paradigm is to fine-tune a model on the task dataset for each programming language. A recent study reported that multilingual fine-tuning benefits a range of tasks and models. However, we find that multilingual fine-tuning leads to performance degradation on recent models UniXcoder and CodeT5. To alleviate the potentially catastrophic forgetting issue in multilingual models, we fix all pre-trained model parameters, insert the parameter-efficient structure adapter, and fine-tune it. Updating only 0.6\% of the overall parameters compared to full-model fine-tuning for each programming language, adapter tuning yields consistent improvements on code search and summarization tasks, achieving state-of-the-art results. In addition, we experimentally show its effectiveness in cross-lingual and low-resource scenarios. Multilingual fine-tuning with 200 samples per programming language approaches the results fine-tuned with the entire dataset on code summarization. Our experiments on three probing tasks show that adapter tuning significantly outperforms full-model fine-tuning and effectively overcomes catastrophic forgetting.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 08:49:54 GMT" } ]
2023-03-29T00:00:00
[ [ "Wang", "Deze", "" ], [ "Chen", "Boxing", "" ], [ "Li", "Shanshan", "" ], [ "Luo", "Wei", "" ], [ "Peng", "Shaoliang", "" ], [ "Dong", "Wei", "" ], [ "Liao", "Xiangke", "" ] ]
new_dataset
0.989683
2303.15848
Zhuoran Zheng
Zhuoran Zheng and Xiuyi Jia
4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free Images
null
null
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by/4.0/
Currently, mobile and IoT devices are in dire need of a series of methods to enhance 4K images with limited resource expenditure. The absence of large-scale 4K benchmark datasets hampers progress in this area, especially for dehazing. The challenges in building ultra-high-definition (UHD) dehazing datasets are the absence of estimation methods for UHD depth maps, high-quality 4K depth estimation datasets, and migration strategies for UHD haze images from synthetic to real domains. To address these problems, we develop a novel synthetic method to simulate 4K hazy images (including nighttime and daytime scenes) from clear images, which first estimates the scene depth, simulates the light rays and object reflectance, then migrates the synthetic images to real domains by using a GAN, and finally yields the hazy effects on 4K resolution images. We wrap these synthesized images into a benchmark called the 4K-HAZE dataset. Specifically, we design the CS-Mixer (an MLP-based model that integrates \textbf{C}hannel domain and \textbf{S}patial domain) to estimate the depth map of 4K clear images, the GU-Net to migrate a 4K synthetic image to the real hazy domain. The most appealing aspect of our approach (depth estimation and domain migration) is the capability to run a 4K image on a single GPU with 24G RAM in real-time (33fps). Additionally, this work presents an objective assessment of several state-of-the-art single-image dehazing methods that are evaluated using the 4K-HAZE dataset. At the end of the paper, we discuss the limitations of the 4K-HAZE dataset and its social implications.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 09:39:29 GMT" } ]
2023-03-29T00:00:00
[ [ "Zheng", "Zhuoran", "" ], [ "Jia", "Xiuyi", "" ] ]
new_dataset
0.999452
2303.15879
Tao Wu
Tao Wu and Mengqi Cao and Ziteng Gao and Gangshan Wu and Limin Wang
STMixer: A One-Stage Sparse Action Detector
Accepted by CVPR 2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by-nc-sa/4.0/
Traditional video action detectors typically adopt the two-stage pipeline, where a person detector is first employed to generate actor boxes and then 3D RoIAlign is used to extract actor-specific features for classification. This detection paradigm requires multi-stage training and inference, and cannot capture context information outside the bounding box. Recently, a few query-based action detectors are proposed to predict action instances in an end-to-end manner. However, they still lack adaptability in feature sampling and decoding, thus suffering from the issues of inferior performance or slower convergence. In this paper, we propose a new one-stage sparse action detector, termed STMixer. STMixer is based on two core designs. First, we present a query-based adaptive feature sampling module, which endows our STMixer with the flexibility of mining a set of discriminative features from the entire spatiotemporal domain. Second, we devise a dual-branch feature mixing module, which allows our STMixer to dynamically attend to and mix video features along the spatial and the temporal dimension respectively for better feature decoding. Coupling these two designs with a video backbone yields an efficient end-to-end action detector. Without bells and whistles, our STMixer obtains the state-of-the-art results on the datasets of AVA, UCF101-24, and JHMDB.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 10:47:06 GMT" } ]
2023-03-29T00:00:00
[ [ "Wu", "Tao", "" ], [ "Cao", "Mengqi", "" ], [ "Gao", "Ziteng", "" ], [ "Wu", "Gangshan", "" ], [ "Wang", "Limin", "" ] ]
new_dataset
0.998757
2303.15892
Yuhao Cheng
Yuhao Cheng and Yichao Yan and Wenhan Zhu and Ye Pan and Bowen Pan and Xiaokang Yang
Head3D: Complete 3D Head Generation via Tri-plane Feature Distillation
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by-nc-sa/4.0/
Head generation with diverse identities is an important task in computer vision and computer graphics, widely used in multimedia applications. However, current full head generation methods require a large number of 3D scans or multi-view images to train the model, resulting in expensive data acquisition cost. To address this issue, we propose Head3D, a method to generate full 3D heads with limited multi-view images. Specifically, our approach first extracts facial priors represented by tri-planes learned in EG3D, a 3D-aware generative model, and then proposes feature distillation to deliver the 3D frontal faces into complete heads without compromising head integrity. To mitigate the domain gap between the face and head models, we present dual-discriminators to guide the frontal and back head generation, respectively. Our model achieves cost-efficient and diverse complete head generation with photo-realistic renderings and high-quality geometry representations. Extensive experiments demonstrate the effectiveness of our proposed Head3D, both qualitatively and quantitatively.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 11:12:26 GMT" } ]
2023-03-29T00:00:00
[ [ "Cheng", "Yuhao", "" ], [ "Yan", "Yichao", "" ], [ "Zhu", "Wenhan", "" ], [ "Pan", "Ye", "" ], [ "Pan", "Bowen", "" ], [ "Yang", "Xiaokang", "" ] ]
new_dataset
0.962042
2303.15900
Jeonghwan Kim
Jeonghwan Kim, Tianyu Li, Sehoon Ha
ARMP: Autoregressive Motion Planning for Quadruped Locomotion and Navigation in Complex Indoor Environments
Submitted to IROS
null
null
null
cs.RO
http://creativecommons.org/licenses/by-nc-sa/4.0/
Generating natural and physically feasible motions for legged robots has been a challenging problem due to its complex dynamics. In this work, we introduce a novel learning-based framework of autoregressive motion planner (ARMP) for quadruped locomotion and navigation. Our method can generate motion plans with an arbitrary length in an autoregressive fashion, unlike most offline trajectory optimization algorithms for a fixed trajectory length. To this end, we first construct the motion library by solving a dense set of trajectory optimization problems for diverse scenarios and parameter settings. Then we learn the motion manifold from the dataset in a supervised learning fashion. We show that the proposed ARMP can generate physically plausible motions for various tasks and situations. We also showcase that our method can be successfully integrated with the recent robot navigation frameworks as a low-level controller and unleash the full capability of legged robots for complex indoor navigation.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 11:26:13 GMT" } ]
2023-03-29T00:00:00
[ [ "Kim", "Jeonghwan", "" ], [ "Li", "Tianyu", "" ], [ "Ha", "Sehoon", "" ] ]
new_dataset
0.99737
2303.15931
Luis Paulo Reis Prof.
Nuno Lau, Luis Paulo Reis, David Simoes, Mohammadreza Kasaei. Miguel Abreu, Tiago Silva, Francisco Resende
FC Portugal 3D Simulation Team: Team Description Paper 2020
null
null
null
null
cs.RO
http://creativecommons.org/licenses/by-nc-nd/4.0/
The FC Portugal 3D team is developed upon the structure of our previous Simulation league 2D/3D teams and our standard platform league team. Our research concerning the robot low-level skills is focused on developing behaviors that may be applied on real robots with minimal adaptation using model-based approaches. Our research on high-level soccer coordination methodologies and team playing is mainly focused on the adaptation of previously developed methodologies from our 2D soccer teams to the 3D humanoid environment and on creating new coordination methodologies based on the previously developed ones. The research-oriented development of our team has been pushing it to be one of the most competitive over the years (World champion in 2000 and Coach Champion in 2002, European champion in 2000 and 2001, Coach 2nd place in 2003 and 2004, European champion in Rescue Simulation and Simulation 3D in 2006, World Champion in Simulation 3D in Bremen 2006 and European champion in 2007, 2012, 2013, 2014 and 2015). This paper describes some of the main innovations of our 3D simulation league team during the last years. A new generic framework for reinforcement learning tasks has also been developed. The current research is focused on improving the above-mentioned framework by developing new learning algorithms to optimize low-level skills, such as running and sprinting. We are also trying to increase student contact by providing reinforcement learning assignments to be completed using our new framework, which exposes a simple interface without sharing low-level implementation details.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 12:41:25 GMT" } ]
2023-03-29T00:00:00
[ [ "Lau", "Nuno", "" ], [ "Reis", "Luis Paulo", "" ], [ "Simoes", "David", "" ], [ "Abreu", "Mohammadreza Kasaei. Miguel", "" ], [ "Silva", "Tiago", "" ], [ "Resende", "Francisco", "" ] ]
new_dataset
0.998521
2303.15935
Xiang Li
Lin Zhao, Lu Zhang, Zihao Wu, Yuzhong Chen, Haixing Dai, Xiaowei Yu, Zhengliang Liu, Tuo Zhang, Xintao Hu, Xi Jiang, Xiang Li, Dajiang Zhu, Dinggang Shen, Tianming Liu
When Brain-inspired AI Meets AGI
null
null
null
null
cs.AI
http://creativecommons.org/licenses/by/4.0/
Artificial General Intelligence (AGI) has been a long-standing goal of humanity, with the aim of creating machines capable of performing any intellectual task that humans can do. To achieve this, AGI researchers draw inspiration from the human brain and seek to replicate its principles in intelligent machines. Brain-inspired artificial intelligence is a field that has emerged from this endeavor, combining insights from neuroscience, psychology, and computer science to develop more efficient and powerful AI systems. In this article, we provide a comprehensive overview of brain-inspired AI from the perspective of AGI. We begin with the current progress in brain-inspired AI and its extensive connection with AGI. We then cover the important characteristics for both human intelligence and AGI (e.g., scaling, multimodality, and reasoning). We discuss important technologies toward achieving AGI in current AI systems, such as in-context learning and prompt tuning. We also investigate the evolution of AGI systems from both algorithmic and infrastructural perspectives. Finally, we explore the limitations and future of AGI.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 12:46:38 GMT" } ]
2023-03-29T00:00:00
[ [ "Zhao", "Lin", "" ], [ "Zhang", "Lu", "" ], [ "Wu", "Zihao", "" ], [ "Chen", "Yuzhong", "" ], [ "Dai", "Haixing", "" ], [ "Yu", "Xiaowei", "" ], [ "Liu", "Zhengliang", "" ], [ "Zhang", "Tuo", "" ], [ "Hu", "Xintao", "" ], [ "Jiang", "Xi", "" ], [ "Li", "Xiang", "" ], [ "Zhu", "Dajiang", "" ], [ "Shen", "Dinggang", "" ], [ "Liu", "Tianming", "" ] ]
new_dataset
0.974155
2303.15937
HsiaoYuan Hsu
HsiaoYuan Hsu, Xiangteng He, Yuxin Peng, Hao Kong and Qing Zhang
PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation Layout
Accepted to CVPR 2023. Dataset and code are available at https://github.com/PKU-ICST-MIPL/PosterLayout-CVPR2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements, including text, logo, and underlay, which is a key to automatic template-free creative graphic design. In practical applications, e.g., poster designs, the canvas is originally non-empty, and both inter-element relationships as well as inter-layer relationships should be concerned when generating a proper layout. A few recent works deal with them simultaneously, but they still suffer from poor graphic performance, such as a lack of layout variety or spatial non-alignment. Since content-aware visual-textual presentation layout is a novel task, we first construct a new dataset named PosterLayout, which consists of 9,974 poster-layout pairs and 905 images, i.e., non-empty canvases. It is more challenging and useful for greater layout variety, domain diversity, and content diversity. Then, we propose design sequence formation (DSF) that reorganizes elements in layouts to imitate the design processes of human designers, and a novel CNN-LSTM-based conditional generative adversarial network (GAN) is presented to generate proper layouts. Specifically, the discriminator is design-sequence-aware and will supervise the "design" process of the generator. Experimental results verify the usefulness of the new benchmark and the effectiveness of the proposed approach, which achieves the best performance by generating suitable layouts for diverse canvases.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 12:48:36 GMT" } ]
2023-03-29T00:00:00
[ [ "Hsu", "HsiaoYuan", "" ], [ "He", "Xiangteng", "" ], [ "Peng", "Yuxin", "" ], [ "Kong", "Hao", "" ], [ "Zhang", "Qing", "" ] ]
new_dataset
0.996635
2303.16055
Frank Regal
Frank Regal, Young Soo Park, Jerry Nolan, Mitch Pryor
Augmented Reality Remote Operation of Dual Arm Manipulators in Hot Boxes
Abstract Submitted to the First International Workshop "Horizons of an Extended Robotics Reality" at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://sites.google.com/view/xr-robotics-iros2022/home?authuser=0
null
null
null
cs.RO
http://creativecommons.org/licenses/by/4.0/
In nuclear isotope and chemistry laboratories, hot cells and gloveboxes provide scientists with a controlled and safe environment to perform experiments. Working on experiments in these isolated containment cells requires scientists to be physically present. For hot cell work today, scientists manipulate equipment and radioactive material inside through a bilateral mechanical control mechanism. Motions produced outside the cell with the master control levers are mechanically transferred to the internal grippers inside the shielded containment cell. There is a growing need to have the capability to conduct experiments within these cells remotely. A simple method to enable remote manipulations within hot cell and glovebox cells is to mount two robotic arms inside a box to mimic the motions of human hands. An AR application was built in this work to allow a user wearing a Microsoft HoloLens 2 headset to teleoperate dual arm manipulators by grasping robotic end-effector digital replicas in AR from a remote location. In addition to the real-time replica of the physical robotic arms in AR, the application enables users to view a live video stream attached to the robotic arms and parse a 3D point cloud of 3D objects in their remote AR environment for better situational awareness. This work also provides users with virtual fixture to assist in manipulation and other teleoperation tasks.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 15:36:06 GMT" } ]
2023-03-29T00:00:00
[ [ "Regal", "Frank", "" ], [ "Park", "Young Soo", "" ], [ "Nolan", "Jerry", "" ], [ "Pryor", "Mitch", "" ] ]
new_dataset
0.997642
2303.16094
Tao Lu
Tao Lu, Xiang Ding, Haisong Liu, Gangshan Wu, Limin Wang
LinK: Linear Kernel for LiDAR-based 3D Perception
Accepted to CVPR2023
null
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
Extending the success of 2D Large Kernel to 3D perception is challenging due to: 1. the cubically-increasing overhead in processing 3D data; 2. the optimization difficulties from data scarcity and sparsity. Previous work has taken the first step to scale up the kernel size from 3x3x3 to 7x7x7 by introducing block-shared weights. However, to reduce the feature variations within a block, it only employs modest block size and fails to achieve larger kernels like the 21x21x21. To address this issue, we propose a new method, called LinK, to achieve a wider-range perception receptive field in a convolution-like manner with two core designs. The first is to replace the static kernel matrix with a linear kernel generator, which adaptively provides weights only for non-empty voxels. The second is to reuse the pre-computed aggregation results in the overlapped blocks to reduce computation complexity. The proposed method successfully enables each voxel to perceive context within a range of 21x21x21. Extensive experiments on two basic perception tasks, 3D object detection and 3D semantic segmentation, demonstrate the effectiveness of our method. Notably, we rank 1st on the public leaderboard of the 3D detection benchmark of nuScenes (LiDAR track), by simply incorporating a LinK-based backbone into the basic detector, CenterPoint. We also boost the strong segmentation baseline's mIoU with 2.7% in the SemanticKITTI test set. Code is available at https://github.com/MCG-NJU/LinK.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 16:02:30 GMT" } ]
2023-03-29T00:00:00
[ [ "Lu", "Tao", "" ], [ "Ding", "Xiang", "" ], [ "Liu", "Haisong", "" ], [ "Wu", "Gangshan", "" ], [ "Wang", "Limin", "" ] ]
new_dataset
0.987567
2303.16098
Marcelo Finger
Maria Clara Ramos Morales Crespo, Maria Lina de Souza Jeannine Rocha, Mariana Louren\c{c}o Sturzeneker, Felipe Ribas Serras, Guilherme Lamartine de Mello, Aline Silva Costa, Mayara Feliciano Palma, Renata Morais Mesquita, Raquel de Paula Guets, Mariana Marques da Silva, Marcelo Finger, Maria Clara Paix\~ao de Sousa, Cristiane Namiuti, Vanessa Martins do Monte
Carolina: a General Corpus of Contemporary Brazilian Portuguese with Provenance, Typology and Versioning Information
14 pages, 3 figures, 1 appendix
null
null
null
cs.CL cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper presents the first publicly available version of the Carolina Corpus and discusses its future directions. Carolina is a large open corpus of Brazilian Portuguese texts under construction using web-as-corpus methodology enhanced with provenance, typology, versioning, and text integrality. The corpus aims at being used both as a reliable source for research in Linguistics and as an important resource for Computer Science research on language models, contributing towards removing Portuguese from the set of low-resource languages. Here we present the construction of the corpus methodology, comparing it with other existing methodologies, as well as the corpus current state: Carolina's first public version has $653,322,577$ tokens, distributed over $7$ broad types. Each text is annotated with several different metadata categories in its header, which we developed using TEI annotation standards. We also present ongoing derivative works and invite NLP researchers to contribute with their own.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 16:09:40 GMT" } ]
2023-03-29T00:00:00
[ [ "Crespo", "Maria Clara Ramos Morales", "" ], [ "Rocha", "Maria Lina de Souza Jeannine", "" ], [ "Sturzeneker", "Mariana Lourenço", "" ], [ "Serras", "Felipe Ribas", "" ], [ "de Mello", "Guilherme Lamartine", "" ], [ "Costa", "Aline Silva", "" ], [ "Palma", "Mayara Feliciano", "" ], [ "Mesquita", "Renata Morais", "" ], [ "Guets", "Raquel de Paula", "" ], [ "da Silva", "Mariana Marques", "" ], [ "Finger", "Marcelo", "" ], [ "de Sousa", "Maria Clara Paixão", "" ], [ "Namiuti", "Cristiane", "" ], [ "Monte", "Vanessa Martins do", "" ] ]
new_dataset
0.987186
2303.16118
Lei Chen
Lei Chen, Zhan Tong, Yibing Song, Gangshan Wu, Limin Wang
CycleACR: Cycle Modeling of Actor-Context Relations for Video Action Detection
technical report
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
The relation modeling between actors and scene context advances video action detection where the correlation of multiple actors makes their action recognition challenging. Existing studies model each actor and scene relation to improve action recognition. However, the scene variations and background interference limit the effectiveness of this relation modeling. In this paper, we propose to select actor-related scene context, rather than directly leverage raw video scenario, to improve relation modeling. We develop a Cycle Actor-Context Relation network (CycleACR) where there is a symmetric graph that models the actor and context relations in a bidirectional form. Our CycleACR consists of the Actor-to-Context Reorganization (A2C-R) that collects actor features for context feature reorganizations, and the Context-to-Actor Enhancement (C2A-E) that dynamically utilizes reorganized context features for actor feature enhancement. Compared to existing designs that focus on C2A-E, our CycleACR introduces A2C-R for a more effective relation modeling. This modeling advances our CycleACR to achieve state-of-the-art performance on two popular action detection datasets (i.e., AVA and UCF101-24). We also provide ablation studies and visualizations as well to show how our cycle actor-context relation modeling improves video action detection. Code is available at https://github.com/MCG-NJU/CycleACR.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 16:40:47 GMT" } ]
2023-03-29T00:00:00
[ [ "Chen", "Lei", "" ], [ "Tong", "Zhan", "" ], [ "Song", "Yibing", "" ], [ "Wu", "Gangshan", "" ], [ "Wang", "Limin", "" ] ]
new_dataset
0.998601
2303.16138
Isabella Huang
Isabella Huang, Yashraj Narang, Ruzena Bajcsy, Fabio Ramos, Tucker Hermans, Dieter Fox
DefGraspNets: Grasp Planning on 3D Fields with Graph Neural Nets
To be published in the IEEE Conference on Robotics and Automation (ICRA), 2023
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Robotic grasping of 3D deformable objects is critical for real-world applications such as food handling and robotic surgery. Unlike rigid and articulated objects, 3D deformable objects have infinite degrees of freedom. Fully defining their state requires 3D deformation and stress fields, which are exceptionally difficult to analytically compute or experimentally measure. Thus, evaluating grasp candidates for grasp planning typically requires accurate, but slow 3D finite element method (FEM) simulation. Sampling-based grasp planning is often impractical, as it requires evaluation of a large number of grasp candidates. Gradient-based grasp planning can be more efficient, but requires a differentiable model to synthesize optimal grasps from initial candidates. Differentiable FEM simulators may fill this role, but are typically no faster than standard FEM. In this work, we propose learning a predictive graph neural network (GNN), DefGraspNets, to act as our differentiable model. We train DefGraspNets to predict 3D stress and deformation fields based on FEM-based grasp simulations. DefGraspNets not only runs up to 1500 times faster than the FEM simulator, but also enables fast gradient-based grasp optimization over 3D stress and deformation metrics. We design DefGraspNets to align with real-world grasp planning practices and demonstrate generalization across multiple test sets, including real-world experiments.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 17:00:45 GMT" } ]
2023-03-29T00:00:00
[ [ "Huang", "Isabella", "" ], [ "Narang", "Yashraj", "" ], [ "Bajcsy", "Ruzena", "" ], [ "Ramos", "Fabio", "" ], [ "Hermans", "Tucker", "" ], [ "Fox", "Dieter", "" ] ]
new_dataset
0.994788
2303.16150
Mario Mart\'inez-Zarzuela
Mario Mart\'inez-Zarzuela, Javier Gonz\'alez-Alonso, M\'iriam Ant\'on-Rodr\'iguez, Francisco J. D\'iaz-Pernas, Henning M\"uller, Cristina Sim\'on-Mart\'inez
VIDIMU. Multimodal video and IMU kinematic dataset on daily life activities using affordable devices
Submitted to journal Scientific Data
null
null
null
cs.CV cs.AI cs.LG eess.IV
http://creativecommons.org/licenses/by/4.0/
Human activity recognition and clinical biomechanics are challenging problems in physical telerehabilitation medicine. However, most publicly available datasets on human body movements cannot be used to study both problems in an out-of-the-lab movement acquisition setting. The objective of the VIDIMU dataset is to pave the way towards affordable patient tracking solutions for remote daily life activities recognition and kinematic analysis. The dataset includes 13 activities registered using a commodity camera and five inertial sensors. The video recordings were acquired in 54 subjects, of which 16 also had simultaneous recordings of inertial sensors. The novelty of VIDIMU lies in: i) the clinical relevance of the chosen movements, ii) the combined utilization of affordable video and custom sensors, and iii) the implementation of state-of-the-art tools for multimodal data processing of 3D body pose tracking and motion reconstruction in a musculoskeletal model from inertial data. The validation confirms that a minimally disturbing acquisition protocol, performed according to real-life conditions can provide a comprehensive picture of human joint angles during daily life activities.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 14:05:49 GMT" } ]
2023-03-29T00:00:00
[ [ "Martínez-Zarzuela", "Mario", "" ], [ "González-Alonso", "Javier", "" ], [ "Antón-Rodríguez", "Míriam", "" ], [ "Díaz-Pernas", "Francisco J.", "" ], [ "Müller", "Henning", "" ], [ "Simón-Martínez", "Cristina", "" ] ]
new_dataset
0.999783
2303.16156
Mao Shi PhD
Mao Shi
On the derivatives of rational B\'{e}zier curves
null
null
null
null
cs.GR
http://creativecommons.org/licenses/by/4.0/
We first point out the defects of the existing derivative formula on the rational B\'{e}zier curve, then propose a new recursive derivative formula, and discuss the expression of derivative formula at the endpoints.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 04:51:30 GMT" } ]
2023-03-29T00:00:00
[ [ "Shi", "Mao", "" ] ]
new_dataset
0.997105
2303.16177
Vedant Mundheda
Vedant Mundheda, Damodar Datta K, Harikumar Kandath
Control Barrier Function-based Predictive Control for Close Proximity operation of UAVs inside a Tunnel
Conference on Automation Science and Engineering (CASE) 2023
null
null
null
cs.RO
http://creativecommons.org/licenses/by/4.0/
This paper introduces a method for effectively controlling the movement of an Unmanned Aerial Vehicle (UAV) within a tunnel. The primary challenge of this problem lies in the UAV's exposure to nonlinear distance-dependent torques and forces generated by the tunnel walls, along with the need to operate safely within a defined region while in close proximity to these walls. To address this problem, the paper proposes the implementation of a Model Predictive Control (MPC) framework with constraints based on Control Barrier Function (CBF). The paper approaches the issue in two distinct ways; first, by maintaining a safe distance from the tunnel walls to avoid the effects of both the walls and ceiling, and second, by minimizing the distance from the walls to effectively manage the nonlinear forces associated with close proximity tasks. Finally, the paper demonstrates the effectiveness of its approach through testing on simulation for various close proximity trajectories with the realistic model of aerodynamic disturbances due to the proximity of the ceiling and boundary walls.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 17:43:32 GMT" } ]
2023-03-29T00:00:00
[ [ "Mundheda", "Vedant", "" ], [ "K", "Damodar Datta", "" ], [ "Kandath", "Harikumar", "" ] ]
new_dataset
0.957277
2303.16202
Vladislav Golyanik
Harshil Bhatia and Edith Tretschk and Zorah L\"ahner and Marcel Seelbach Benkner and Michael Moeller and Christian Theobalt and Vladislav Golyanik
CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes
Computer Vision and Pattern Recognition (CVPR) 2023; 22 pages, 24 figures and 5 tables; Project page: https://4dqv.mpi-inf.mpg.de/CCuantuMM/
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Jointly matching multiple, non-rigidly deformed 3D shapes is a challenging, $\mathcal{NP}$-hard problem. A perfect matching is necessarily cycle-consistent: Following the pairwise point correspondences along several shapes must end up at the starting vertex of the original shape. Unfortunately, existing quantum shape-matching methods do not support multiple shapes and even less cycle consistency. This paper addresses the open challenges and introduces the first quantum-hybrid approach for 3D shape multi-matching; in addition, it is also cycle-consistent. Its iterative formulation is admissible to modern adiabatic quantum hardware and scales linearly with the total number of input shapes. Both these characteristics are achieved by reducing the $N$-shape case to a sequence of three-shape matchings, the derivation of which is our main technical contribution. Thanks to quantum annealing, high-quality solutions with low energy are retrieved for the intermediate $\mathcal{NP}$-hard objectives. On benchmark datasets, the proposed approach significantly outperforms extensions to multi-shape matching of a previous quantum-hybrid two-shape matching method and is on-par with classical multi-matching methods.
[ { "version": "v1", "created": "Tue, 28 Mar 2023 17:59:55 GMT" } ]
2023-03-29T00:00:00
[ [ "Bhatia", "Harshil", "" ], [ "Tretschk", "Edith", "" ], [ "Lähner", "Zorah", "" ], [ "Benkner", "Marcel Seelbach", "" ], [ "Moeller", "Michael", "" ], [ "Theobalt", "Christian", "" ], [ "Golyanik", "Vladislav", "" ] ]
new_dataset
0.991157
2112.05301
Qing Li
Qing Li, Xiaojiang Peng, Chuan Yan, Pan Gao, Qi Hao
Self-Ensemling for 3D Point Cloud Domain Adaption
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recently 3D point cloud learning has been a hot topic in computer vision and autonomous driving. Due to the fact that it is difficult to manually annotate a qualitative large-scale 3D point cloud dataset, unsupervised domain adaptation (UDA) is popular in 3D point cloud learning which aims to transfer the learned knowledge from the labeled source domain to the unlabeled target domain. However, the generalization and reconstruction errors caused by domain shift with simply-learned model are inevitable which substantially hinder the model's capability from learning good representations. To address these issues, we propose an end-to-end self-ensembling network (SEN) for 3D point cloud domain adaption tasks. Generally, our SEN resorts to the advantages of Mean Teacher and semi-supervised learning, and introduces a soft classification loss and a consistency loss, aiming to achieve consistent generalization and accurate reconstruction. In SEN, a student network is kept in a collaborative manner with supervised learning and self-supervised learning, and a teacher network conducts temporal consistency to learn useful representations and ensure the quality of point clouds reconstruction. Extensive experiments on several 3D point cloud UDA benchmarks show that our SEN outperforms the state-of-the-art methods on both classification and segmentation tasks. Moreover, further analysis demonstrates that our SEN also achieves better reconstruction results.
[ { "version": "v1", "created": "Fri, 10 Dec 2021 02:18:09 GMT" }, { "version": "v2", "created": "Sat, 25 Mar 2023 03:19:53 GMT" } ]
2023-03-28T00:00:00
[ [ "Li", "Qing", "" ], [ "Peng", "Xiaojiang", "" ], [ "Yan", "Chuan", "" ], [ "Gao", "Pan", "" ], [ "Hao", "Qi", "" ] ]
new_dataset
0.998315
2201.01439
Masaaki Harada
Masaaki Harada
Construction of extremal Type II $\mathbb{Z}_{2k}$-codes
25 pages
null
10.1016/j.ffa.2022.102154
null
cs.IT math.CO math.IT
http://creativecommons.org/licenses/by/4.0/
We give methods for constructing many self-dual $\mathbb{Z}_m$-codes and Type II $\mathbb{Z}_{2k}$-codes of length $2n$ starting from a given self-dual $\mathbb{Z}_m$-code and Type II $\mathbb{Z}_{2k}$-code of length $2n$, respectively. As an application, we construct extremal Type II $\mathbb{Z}_{2k}$-codes of length $24$ for $k=4,5,\ldots,20$ and extremal Type II $\mathbb{Z}_{2k}$-codes of length $32$ for $k=4,5,\ldots,10$. We also construct new extremal Type II $\mathbb{Z}_4$-codes of lengths $56$ and $64$.
[ { "version": "v1", "created": "Wed, 5 Jan 2022 03:53:08 GMT" }, { "version": "v2", "created": "Sat, 18 Jun 2022 06:31:40 GMT" } ]
2023-03-28T00:00:00
[ [ "Harada", "Masaaki", "" ] ]
new_dataset
0.99916
2202.04110
Guangyao Zhou
Guangyao Zhou, Antoine Dedieu, Nishanth Kumar, Wolfgang Lehrach, Miguel L\'azaro-Gredilla, Shrinu Kushagra, Dileep George
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX
Update authors list
null
null
null
cs.LG cs.AI stat.ML
http://creativecommons.org/licenses/by/4.0/
PGMax is an open-source Python package for (a) easily specifying discrete Probabilistic Graphical Models (PGMs) as factor graphs; and (b) automatically running efficient and scalable loopy belief propagation (LBP) in JAX. PGMax supports general factor graphs with tractable factors, and leverages modern accelerators like GPUs for inference. Compared with existing alternatives, PGMax obtains higher-quality inference results with up to three orders-of-magnitude inference time speedups. PGMax additionally interacts seamlessly with the rapidly growing JAX ecosystem, opening up new research possibilities. Our source code, examples and documentation are available at https://github.com/deepmind/PGMax.
[ { "version": "v1", "created": "Tue, 8 Feb 2022 19:27:48 GMT" }, { "version": "v2", "created": "Fri, 6 May 2022 19:15:22 GMT" }, { "version": "v3", "created": "Mon, 13 Mar 2023 17:20:47 GMT" }, { "version": "v4", "created": "Fri, 24 Mar 2023 23:34:02 GMT" } ]
2023-03-28T00:00:00
[ [ "Zhou", "Guangyao", "" ], [ "Dedieu", "Antoine", "" ], [ "Kumar", "Nishanth", "" ], [ "Lehrach", "Wolfgang", "" ], [ "Lázaro-Gredilla", "Miguel", "" ], [ "Kushagra", "Shrinu", "" ], [ "George", "Dileep", "" ] ]
new_dataset
0.990811
2203.00456
Esther Rituerto-Gonz\'alez
Jose A. Miranda, Esther Rituerto-Gonz\'alez, Laura Guti\'errez-Mart\'in, Clara Luis-Mingueza, Manuel F. Canabal, Alberto Ram\'irez B\'arcenas, Jose M. Lanza-Guti\'errez, Carmen Pel\'aez-Moreno, Celia L\'opez-Ongil
WEMAC: Women and Emotion Multi-modal Affective Computing dataset
null
null
null
null
cs.HC eess.SP
http://creativecommons.org/licenses/by/4.0/
Among the seventeen Sustainable Development Goals (SDGs) proposed within the 2030 Agenda and adopted by all the United Nations member states, the Fifth SDG is a call for action to turn Gender Equality into a fundamental human right and an essential foundation for a better world. It includes the eradication of all types of violence against women. Within this context, the UC3M4Safety research team aims to develop Bindi. This is a cyber-physical system which includes embedded Artificial Intelligence algorithms, for user real-time monitoring towards the detection of affective states, with the ultimate goal of achieving the early detection of risk situations for women. On this basis, we make use of wearable affective computing including smart sensors, data encryption for secure and accurate collection of presumed crime evidence, as well as the remote connection to protecting agents. Towards the development of such system, the recordings of different laboratory and into-the-wild datasets are in process. These are contained within the UC3M4Safety Database. Thus, this paper presents and details the first release of WEMAC, a novel multi-modal dataset, which comprises a laboratory-based experiment for 47 women volunteers that were exposed to validated audio-visual stimuli to induce real emotions by using a virtual reality headset while physiological, speech signals and self-reports were acquired and collected. We believe this dataset will serve and assist research on multi-modal affective computing using physiological and speech information.
[ { "version": "v1", "created": "Tue, 1 Mar 2022 13:39:54 GMT" }, { "version": "v2", "created": "Wed, 8 Jun 2022 14:31:54 GMT" }, { "version": "v3", "created": "Mon, 27 Mar 2023 10:19:49 GMT" } ]
2023-03-28T00:00:00
[ [ "Miranda", "Jose A.", "" ], [ "Rituerto-González", "Esther", "" ], [ "Gutiérrez-Martín", "Laura", "" ], [ "Luis-Mingueza", "Clara", "" ], [ "Canabal", "Manuel F.", "" ], [ "Bárcenas", "Alberto Ramírez", "" ], [ "Lanza-Gutiérrez", "Jose M.", "" ], [ "Peláez-Moreno", "Carmen", "" ], [ "López-Ongil", "Celia", "" ] ]
new_dataset
0.999765
2204.03648
Yifan Wang
Yifan Wang, Aleksander Holynski, Xiuming Zhang and Xuaner Zhang
SunStage: Portrait Reconstruction and Relighting using the Sun as a Light Stage
CVPR 2023. Project page: https://sunstage.cs.washington.edu/
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
A light stage uses a series of calibrated cameras and lights to capture a subject's facial appearance under varying illumination and viewpoint. This captured information is crucial for facial reconstruction and relighting. Unfortunately, light stages are often inaccessible: they are expensive and require significant technical expertise for construction and operation. In this paper, we present SunStage: a lightweight alternative to a light stage that captures comparable data using only a smartphone camera and the sun. Our method only requires the user to capture a selfie video outdoors, rotating in place, and uses the varying angles between the sun and the face as guidance in joint reconstruction of facial geometry, reflectance, camera pose, and lighting parameters. Despite the in-the-wild un-calibrated setting, our approach is able to reconstruct detailed facial appearance and geometry, enabling compelling effects such as relighting, novel view synthesis, and reflectance editing. Results and interactive demos are available at https://sunstage.cs.washington.edu/.
[ { "version": "v1", "created": "Thu, 7 Apr 2022 17:59:51 GMT" }, { "version": "v2", "created": "Fri, 24 Mar 2023 22:54:08 GMT" } ]
2023-03-28T00:00:00
[ [ "Wang", "Yifan", "" ], [ "Holynski", "Aleksander", "" ], [ "Zhang", "Xiuming", "" ], [ "Zhang", "Xuaner", "" ] ]
new_dataset
0.997737
2204.08179
Haoying Li
Haoying Li, Ziran Zhang, Tingting Jiang, Peng Luo, Huajun Feng, Zhihai Xu
Real-World Deep Local Motion Deblurring
Accept by AAAI-2023 (Oral)
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Most existing deblurring methods focus on removing global blur caused by camera shake, while they cannot well handle local blur caused by object movements. To fill the vacancy of local deblurring in real scenes, we establish the first real local motion blur dataset (ReLoBlur), which is captured by a synchronized beam-splitting photographing system and corrected by a post-progressing pipeline. Based on ReLoBlur, we propose a Local Blur-Aware Gated network (LBAG) and several local blur-aware techniques to bridge the gap between global and local deblurring: 1) a blur detection approach based on background subtraction to localize blurred regions; 2) a gate mechanism to guide our network to focus on blurred regions; and 3) a blur-aware patch cropping strategy to address data imbalance problem. Extensive experiments prove the reliability of ReLoBlur dataset, and demonstrate that LBAG achieves better performance than state-of-the-art global deblurring methods without our proposed local blur-aware techniques.
[ { "version": "v1", "created": "Mon, 18 Apr 2022 06:24:02 GMT" }, { "version": "v2", "created": "Sun, 26 Mar 2023 16:33:55 GMT" } ]
2023-03-28T00:00:00
[ [ "Li", "Haoying", "" ], [ "Zhang", "Ziran", "" ], [ "Jiang", "Tingting", "" ], [ "Luo", "Peng", "" ], [ "Feng", "Huajun", "" ], [ "Xu", "Zhihai", "" ] ]
new_dataset
0.998826
2204.11964
Pinaki Nath Chowdhury
Pinaki Nath Chowdhury and Ayan Kumar Bhunia and Aneeshan Sain and Subhadeep Koley and Tao Xiang and Yi-Zhe Song
SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Text
Accepted in Computer Vision and Pattern Recognition (CVPR), 2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
In this paper, we extend scene understanding to include that of human sketch. The result is a complete trilogy of scene representation from three diverse and complementary modalities -- sketch, photo, and text. Instead of learning a rigid three-way embedding and be done with it, we focus on learning a flexible joint embedding that fully supports the ``optionality" that this complementarity brings. Our embedding supports optionality on two axes: (i) optionality across modalities -- use any combination of modalities as query for downstream tasks like retrieval, (ii) optionality across tasks -- simultaneously utilising the embedding for either discriminative (e.g., retrieval) or generative tasks (e.g., captioning). This provides flexibility to end-users by exploiting the best of each modality, therefore serving the very purpose behind our proposal of a trilogy in the first place. First, a combination of information-bottleneck and conditional invertible neural networks disentangle the modality-specific component from modality-agnostic in sketch, photo, and text. Second, the modality-agnostic instances from sketch, photo, and text are synergised using a modified cross-attention. Once learned, we show our embedding can accommodate a multi-facet of scene-related tasks, including those enabled for the first time by the inclusion of sketch, all without any task-specific modifications. Project Page: \url{http://www.pinakinathc.me/scenetrilogy}
[ { "version": "v1", "created": "Mon, 25 Apr 2022 20:58:17 GMT" }, { "version": "v2", "created": "Tue, 6 Dec 2022 01:36:47 GMT" }, { "version": "v3", "created": "Sun, 26 Mar 2023 13:01:15 GMT" } ]
2023-03-28T00:00:00
[ [ "Chowdhury", "Pinaki Nath", "" ], [ "Bhunia", "Ayan Kumar", "" ], [ "Sain", "Aneeshan", "" ], [ "Koley", "Subhadeep", "" ], [ "Xiang", "Tao", "" ], [ "Song", "Yi-Zhe", "" ] ]
new_dataset
0.979683
2205.09336
Albin Dahlin
Albin Dahlin and Yiannis Karayiannidis
Creating Star Worlds: Reshaping the Robot Workspace for Online Motion Planning
null
null
null
null
cs.RO
http://creativecommons.org/licenses/by/4.0/
Motion planning methods like navigation functions and harmonic potential fields provide (almost) global convergence and are suitable for obstacle avoidance in dynamically changing environments due to their reactive nature. A common assumption in the control design is that the robot operates in a disjoint star world, i.e. all obstacles are strictly starshaped and mutually disjoint. However, in real-life scenarios obstacles may intersect due to expanded obstacle regions corresponding to robot radius or safety margins. To broaden the applicability of aforementioned reactive motion planning methods, we propose a method to reshape a workspace of intersecting obstacles into a disjoint star world. The algorithm is based on two novel concepts presented here, namely admissible kernel and starshaped hull with specified kernel, which are closely related to the notion of starshaped hull. The utilization of the proposed method is illustrated with examples of a robot operating in a 2D workspace using a harmonic potential field approach in combination with the developed algorithm.
[ { "version": "v1", "created": "Thu, 19 May 2022 06:26:47 GMT" }, { "version": "v2", "created": "Mon, 27 Mar 2023 09:38:21 GMT" } ]
2023-03-28T00:00:00
[ [ "Dahlin", "Albin", "" ], [ "Karayiannidis", "Yiannis", "" ] ]
new_dataset
0.999396
2206.03285
Jinkun Geng
Jinkun Geng and Anirudh Sivaraman and Balaji Prabhakar and Mendel Rosenblum
Nezha: Deployable and High-Performance Consensus Using Synchronized Clocks
Accepted by 49th International Conference on Very Large Data Bases (VLDB 2023)
null
null
null
cs.DC cs.DB cs.NI
http://creativecommons.org/licenses/by-nc-sa/4.0/
This paper presents a high-performance consensus protocol, Nezha, which can be deployed by cloud tenants without any support from their cloud provider. Nezha bridges the gap between protocols such as Multi-Paxos and Raft, which can be readily deployed and protocols such as NOPaxos and Speculative Paxos, that provide better performance, but require access to technologies such as programmable switches and in-network prioritization, which cloud tenants do not have. Nezha uses a new multicast primitive called deadline-ordered multicast (DOM). DOM uses high-accuracy software clock synchronization to synchronize sender and receiver clocks. Senders tag messages with deadlines in synchronized time; receivers process messages in deadline order, on or after their deadline. We compare Nezha with Multi-Paxos, Fast Paxos, Raft, a NOPaxos version we optimized for the cloud, and 2 recent protocols, Domino and TOQ-EPaxos, that use synchronized clocks. In throughput, Nezha outperforms all baselines by a median of 5.4x (range: 1.9-20.9x). In latency, Nezha outperforms five baselines by a median of 2.3x (range: 1.3-4.0x), with one exception: it sacrifices 33% latency compared with our optimized NOPaxos in one test. We also prototype two applications, a key-value store and a fair-access stock exchange, on top of Nezha to show that Nezha only modestly reduces their performance relative to an unreplicated system. Nezha is available at https://github.com/Steamgjk/Nezha.
[ { "version": "v1", "created": "Fri, 3 Jun 2022 21:30:33 GMT" }, { "version": "v10", "created": "Tue, 27 Dec 2022 21:08:54 GMT" }, { "version": "v11", "created": "Fri, 24 Mar 2023 17:20:38 GMT" }, { "version": "v2", "created": "Wed, 8 Jun 2022 00:38:11 GMT" }, { "version": "v3", "created": "Mon, 25 Jul 2022 23:23:03 GMT" }, { "version": "v4", "created": "Tue, 2 Aug 2022 23:50:40 GMT" }, { "version": "v5", "created": "Thu, 4 Aug 2022 23:15:05 GMT" }, { "version": "v6", "created": "Thu, 25 Aug 2022 06:21:08 GMT" }, { "version": "v7", "created": "Sat, 8 Oct 2022 07:00:43 GMT" }, { "version": "v8", "created": "Sat, 15 Oct 2022 06:58:05 GMT" }, { "version": "v9", "created": "Mon, 19 Dec 2022 00:56:50 GMT" } ]
2023-03-28T00:00:00
[ [ "Geng", "Jinkun", "" ], [ "Sivaraman", "Anirudh", "" ], [ "Prabhakar", "Balaji", "" ], [ "Rosenblum", "Mendel", "" ] ]
new_dataset
0.999587
2206.07817
Jennifer Volk
Jennifer Volk, George Tzimpragos, Alex Wynn, Evan Golden and Timothy Sherwood
Low-Cost Superconducting Fan-Out with Cell I$_\text{C}$ Ranking
12 pages, 20 figures, accepted at IEEE TAS
null
10.1109/TASC.2023.3256797
null
cs.ET
http://creativecommons.org/licenses/by/4.0/
Superconductor electronics (SCE) promise computer systems with orders of magnitude higher speeds and lower energy consumption than their complementary metal-oxide semiconductor (CMOS) counterparts. At the same time, the scalability and resource utilization of superconducting systems are major concerns. Some of these concerns come from device-level challenges and the gap between SCE and CMOS technology nodes, and others come from the way Josephson Junctions (JJs) are used. Towards this end, we notice that a considerable fraction of hardware resources are not involved in logic operations, but rather are used for fan-out and buffering purposes. In this paper, we ask if there is a way to reduce these overheads, propose the use of JJs at the cell boundaries to increase the number of outputs that a single stage can drive, and establish a set of rules to discretize critical currents in a way that is conducive to this assignment. Finally, we explore the design trade-offs that the presented approach opens up and demonstrate its promise through detailed analog simulations and modeling analyses. Our experiments indicate that the introduced method leads to a 48% savings in the JJ count for a tree with a fan-out of 1024, as well as an average of 43% of the JJ count for signal splitting and 32% for clock splitting in ISCAS'85 benchmarks.
[ { "version": "v1", "created": "Wed, 15 Jun 2022 21:08:16 GMT" }, { "version": "v2", "created": "Wed, 19 Oct 2022 19:47:27 GMT" }, { "version": "v3", "created": "Thu, 27 Oct 2022 07:29:24 GMT" }, { "version": "v4", "created": "Mon, 27 Mar 2023 16:26:47 GMT" } ]
2023-03-28T00:00:00
[ [ "Volk", "Jennifer", "" ], [ "Tzimpragos", "George", "" ], [ "Wynn", "Alex", "" ], [ "Golden", "Evan", "" ], [ "Sherwood", "Timothy", "" ] ]
new_dataset
0.982079
2207.01610
Weicai Ye
Weicai Ye, Xinyue Lan, Shuo Chen, Yuhang Ming, Xingyuan Yu, Hujun Bao, Zhaopeng Cui, Guofeng Zhang
PVO: Panoptic Visual Odometry
CVPR2023 Project page: https://zju3dv.github.io/pvo/ code: https://github.com/zju3dv/PVO
null
null
null
cs.CV cs.RO
http://creativecommons.org/licenses/by-nc-sa/4.0/
We present PVO, a novel panoptic visual odometry framework to achieve more comprehensive modeling of the scene motion, geometry, and panoptic segmentation information. Our PVO models visual odometry (VO) and video panoptic segmentation (VPS) in a unified view, which makes the two tasks mutually beneficial. Specifically, we introduce a panoptic update module into the VO Module with the guidance of image panoptic segmentation. This Panoptic-Enhanced VO Module can alleviate the impact of dynamic objects in the camera pose estimation with a panoptic-aware dynamic mask. On the other hand, the VO-Enhanced VPS Module also improves the segmentation accuracy by fusing the panoptic segmentation result of the current frame on the fly to the adjacent frames, using geometric information such as camera pose, depth, and optical flow obtained from the VO Module. These two modules contribute to each other through recurrent iterative optimization. Extensive experiments demonstrate that PVO outperforms state-of-the-art methods in both visual odometry and video panoptic segmentation tasks.
[ { "version": "v1", "created": "Mon, 4 Jul 2022 17:51:39 GMT" }, { "version": "v2", "created": "Sun, 26 Mar 2023 17:47:21 GMT" } ]
2023-03-28T00:00:00
[ [ "Ye", "Weicai", "" ], [ "Lan", "Xinyue", "" ], [ "Chen", "Shuo", "" ], [ "Ming", "Yuhang", "" ], [ "Yu", "Xingyuan", "" ], [ "Bao", "Hujun", "" ], [ "Cui", "Zhaopeng", "" ], [ "Zhang", "Guofeng", "" ] ]
new_dataset
0.996213
2208.00339
Jiang Li
Jiang Li, Xiaoping Wang, Guoqing Lv, Zhigang Zeng
GraphMFT: A Graph Network based Multimodal Fusion Technique for Emotion Recognition in Conversation
12 pages
null
null
null
cs.MM
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Multimodal machine learning is an emerging area of research, which has received a great deal of scholarly attention in recent years. Up to now, there are few studies on multimodal conversational emotion recognition. Since Graph Neural Networks (GNNs) possess the powerful capacity of relational modeling, they have an inherent advantage in the field of multimodal learning. GNNs leverage the graph constructed from multimodal data to perform intra- and inter-modal information interaction, which effectively facilitates the integration and complementation of multimodal data. In this work, we propose a novel Graph network based Multimodal Fusion Technique (GraphMFT) for emotion recognition in conversation. Multimodal data can be modeled as a graph, where each data object is regarded as a node, and both intra- and inter-modal dependencies existing between data objects can be regarded as edges. GraphMFT utilizes multiple improved graph attention networks to capture intra-modal contextual information and inter-modal complementary information. In addition, the proposed GraphMFT attempts to address the challenges of existing graph-based multimodal Emotion Recognition in Conversation (ERC) models such as MMGCN. Empirical results on two public multimodal datasets reveal that our model outperforms the State-Of-The-Art (SOTA) approaches with the accuracy of 67.90% and 61.30%.
[ { "version": "v1", "created": "Sun, 31 Jul 2022 02:23:24 GMT" }, { "version": "v2", "created": "Wed, 10 Aug 2022 16:38:30 GMT" }, { "version": "v3", "created": "Sun, 26 Mar 2023 03:32:28 GMT" } ]
2023-03-28T00:00:00
[ [ "Li", "Jiang", "" ], [ "Wang", "Xiaoping", "" ], [ "Lv", "Guoqing", "" ], [ "Zeng", "Zhigang", "" ] ]
new_dataset
0.96602
2210.00445
Hao Wang
Hao Wang, Guosheng Lin, Ana Garc\'ia del Molino, Anran Wang, Jiashi Feng, Zhiqi Shen
ManiCLIP: Multi-Attribute Face Manipulation from Text
Code link: https://github.com/hwang1996/ManiCLIP
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper we present a novel multi-attribute face manipulation method based on textual descriptions. Previous text-based image editing methods either require test-time optimization for each individual image or are restricted to single attribute editing. Extending these methods to multi-attribute face image editing scenarios will introduce undesired excessive attribute change, e.g., text-relevant attributes are overly manipulated and text-irrelevant attributes are also changed. In order to address these challenges and achieve natural editing over multiple face attributes, we propose a new decoupling training scheme where we use group sampling to get text segments from same attribute categories, instead of whole complex sentences. Further, to preserve other existing face attributes, we encourage the model to edit the latent code of each attribute separately via an entropy constraint. During the inference phase, our model is able to edit new face images without any test-time optimization, even from complex textual prompts. We show extensive experiments and analysis to demonstrate the efficacy of our method, which generates natural manipulated faces with minimal text-irrelevant attribute editing. Code and pre-trained model are available at https://github.com/hwang1996/ManiCLIP.
[ { "version": "v1", "created": "Sun, 2 Oct 2022 07:22:55 GMT" }, { "version": "v2", "created": "Mon, 28 Nov 2022 01:29:52 GMT" }, { "version": "v3", "created": "Sun, 26 Mar 2023 01:52:42 GMT" } ]
2023-03-28T00:00:00
[ [ "Wang", "Hao", "" ], [ "Lin", "Guosheng", "" ], [ "del Molino", "Ana García", "" ], [ "Wang", "Anran", "" ], [ "Feng", "Jiashi", "" ], [ "Shen", "Zhiqi", "" ] ]
new_dataset
0.995612
2210.01781
Boxiao Pan
Boxiao Pan, Bokui Shen, Davis Rempe, Despoina Paschalidou, Kaichun Mo, Yanchao Yang, Leonidas J. Guibas
COPILOT: Human-Environment Collision Prediction and Localization from Egocentric Videos
null
null
null
null
cs.CV cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The ability to forecast human-environment collisions from egocentric observations is vital to enable collision avoidance in applications such as VR, AR, and wearable assistive robotics. In this work, we introduce the challenging problem of predicting collisions in diverse environments from multi-view egocentric videos captured from body-mounted cameras. Solving this problem requires a generalizable perception system that can classify which human body joints will collide and estimate a collision region heatmap to localize collisions in the environment. To achieve this, we propose a transformer-based model called COPILOT to perform collision prediction and localization simultaneously, which accumulates information across multi-view inputs through a novel 4D space-time-viewpoint attention mechanism. To train our model and enable future research on this task, we develop a synthetic data generation framework that produces egocentric videos of virtual humans moving and colliding within diverse 3D environments. This framework is then used to establish a large-scale dataset consisting of 8.6M egocentric RGBD frames. Extensive experiments show that COPILOT generalizes to unseen synthetic as well as real-world scenes. We further demonstrate COPILOT outputs are useful for downstream collision avoidance through simple closed-loop control. Please visit our project webpage at https://sites.google.com/stanford.edu/copilot.
[ { "version": "v1", "created": "Tue, 4 Oct 2022 17:49:23 GMT" }, { "version": "v2", "created": "Sun, 26 Mar 2023 05:27:31 GMT" } ]
2023-03-28T00:00:00
[ [ "Pan", "Boxiao", "" ], [ "Shen", "Bokui", "" ], [ "Rempe", "Davis", "" ], [ "Paschalidou", "Despoina", "" ], [ "Mo", "Kaichun", "" ], [ "Yang", "Yanchao", "" ], [ "Guibas", "Leonidas J.", "" ] ]
new_dataset
0.97526
2211.05975
Qiao Xiang
Qiang Li, Qiao Xiang, Derui Liu, Yuxin Wang, Haonan Qiu, Xiaoliang Wang, Jie Zhang, Ridi Wen, Haohao Song, Gexiao Tian, Chenyang Huang, Lulu Chen, Shaozong Liu, Yaohui Wu, Zhiwu Wu, Zicheng Luo, Yuchao Shao, Chao Han, Zhongjie Wu, Jianbo Dong, Zheng Cao, Jinbo Wu, Jiwu Shu, Jiesheng Wu
From RDMA to RDCA: Toward High-Speed Last Mile of Data Center Networks Using Remote Direct Cache Access
null
null
null
null
cs.NI cs.DC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we conduct systematic measurement studies to show that the high memory bandwidth consumption of modern distributed applications can lead to a significant drop of network throughput and a large increase of tail latency in high-speed RDMA networks.We identify its root cause as the high contention of memory bandwidth between application processes and network processes. This contention leads to frequent packet drops at the NIC of receiving hosts, which triggers the congestion control mechanism of the network and eventually results in network performance degradation. To tackle this problem, we make a key observation that given the distributed storage service, the vast majority of data it receives from the network will be eventually written to high-speed storage media (e.g., SSD) by CPU. As such, we propose to bypass host memory when processing received data to completely circumvent this performance bottleneck. In particular, we design Lamda, a novel receiver cache processing system that consumes a small amount of CPU cache to process received data from the network at line rate. We implement a prototype of Lamda and evaluate its performance extensively in a Clos-based testbed. Results show that for distributed storage applications, Lamda improves network throughput by 4.7% with zero memory bandwidth consumption on storage nodes, and improves network throughput by up 17% and 45% for large block size and small size under the memory bandwidth pressure, respectively. Lamda can also be applied to latency-sensitive HPC applications, which reduces their communication latency by 35.1%.
[ { "version": "v1", "created": "Fri, 11 Nov 2022 02:43:50 GMT" }, { "version": "v2", "created": "Sat, 25 Mar 2023 05:52:24 GMT" } ]
2023-03-28T00:00:00
[ [ "Li", "Qiang", "" ], [ "Xiang", "Qiao", "" ], [ "Liu", "Derui", "" ], [ "Wang", "Yuxin", "" ], [ "Qiu", "Haonan", "" ], [ "Wang", "Xiaoliang", "" ], [ "Zhang", "Jie", "" ], [ "Wen", "Ridi", "" ], [ "Song", "Haohao", "" ], [ "Tian", "Gexiao", "" ], [ "Huang", "Chenyang", "" ], [ "Chen", "Lulu", "" ], [ "Liu", "Shaozong", "" ], [ "Wu", "Yaohui", "" ], [ "Wu", "Zhiwu", "" ], [ "Luo", "Zicheng", "" ], [ "Shao", "Yuchao", "" ], [ "Han", "Chao", "" ], [ "Wu", "Zhongjie", "" ], [ "Dong", "Jianbo", "" ], [ "Cao", "Zheng", "" ], [ "Wu", "Jinbo", "" ], [ "Shu", "Jiwu", "" ], [ "Wu", "Jiesheng", "" ] ]
new_dataset
0.997449
2211.11177
Shitao Tang
Shitao Tang, Sicong Tang, Andrea Tagliasacchi, Ping Tan and Yasutaka Furukawa
NeuMap: Neural Coordinate Mapping by Auto-Transdecoder for Camera Localization
CVPR2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
This paper presents an end-to-end neural mapping method for camera localization, dubbed NeuMap, encoding a whole scene into a grid of latent codes, with which a Transformer-based auto-decoder regresses 3D coordinates of query pixels. State-of-the-art feature matching methods require each scene to be stored as a 3D point cloud with per-point features, consuming several gigabytes of storage per scene. While compression is possible, performance drops significantly at high compression rates. Conversely, coordinate regression methods achieve high compression by storing scene information in a neural network but suffer from reduced robustness. NeuMap combines the advantages of both approaches by utilizing 1) learnable latent codes for efficient scene representation and 2) a scene-agnostic Transformer-based auto-decoder to infer coordinates for query pixels. This scene-agnostic network design learns robust matching priors from large-scale data and enables rapid optimization of codes for new scenes while keeping the network weights fixed. Extensive evaluations on five benchmarks show that NeuMap significantly outperforms other coordinate regression methods and achieves comparable performance to feature matching methods while requiring a much smaller scene representation size. For example, NeuMap achieves 39.1% accuracy in the Aachen night benchmark with only 6MB of data, whereas alternative methods require 100MB or several gigabytes and fail completely under high compression settings. The codes are available at https://github.com/Tangshitao/NeuMap
[ { "version": "v1", "created": "Mon, 21 Nov 2022 04:46:22 GMT" }, { "version": "v2", "created": "Sun, 26 Mar 2023 06:22:15 GMT" } ]
2023-03-28T00:00:00
[ [ "Tang", "Shitao", "" ], [ "Tang", "Sicong", "" ], [ "Tagliasacchi", "Andrea", "" ], [ "Tan", "Ping", "" ], [ "Furukawa", "Yasutaka", "" ] ]
new_dataset
0.987654
2211.11646
Junkai Huang
Benran Hu, Junkai Huang, Yichen Liu, Yu-Wing Tai, Chi-Keung Tang
NeRF-RPN: A general framework for object detection in NeRFs
Accepted by CVPR 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper presents the first significant object detection framework, NeRF-RPN, which directly operates on NeRF. Given a pre-trained NeRF model, NeRF-RPN aims to detect all bounding boxes of objects in a scene. By exploiting a novel voxel representation that incorporates multi-scale 3D neural volumetric features, we demonstrate it is possible to regress the 3D bounding boxes of objects in NeRF directly without rendering the NeRF at any viewpoint. NeRF-RPN is a general framework and can be applied to detect objects without class labels. We experimented NeRF-RPN with various backbone architectures, RPN head designs and loss functions. All of them can be trained in an end-to-end manner to estimate high quality 3D bounding boxes. To facilitate future research in object detection for NeRF, we built a new benchmark dataset which consists of both synthetic and real-world data with careful labeling and clean up. Code and dataset are available at https://github.com/lyclyc52/NeRF_RPN.
[ { "version": "v1", "created": "Mon, 21 Nov 2022 17:02:01 GMT" }, { "version": "v2", "created": "Tue, 22 Nov 2022 01:55:00 GMT" }, { "version": "v3", "created": "Mon, 27 Mar 2023 16:40:30 GMT" } ]
2023-03-28T00:00:00
[ [ "Hu", "Benran", "" ], [ "Huang", "Junkai", "" ], [ "Liu", "Yichen", "" ], [ "Tai", "Yu-Wing", "" ], [ "Tang", "Chi-Keung", "" ] ]
new_dataset
0.988258
2211.12054
Yuan Yao
Yuan Yao, Tianyu Yu, Ao Zhang, Mengdi Li, Ruobing Xie, Cornelius Weber, Zhiyuan Liu, Hai-Tao Zheng, Stefan Wermter, Tat-Seng Chua, Maosong Sun
Visually Grounded Commonsense Knowledge Acquisition
Accepted by AAAI 2023
null
null
null
cs.CV cs.AI cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Large-scale commonsense knowledge bases empower a broad range of AI applications, where the automatic extraction of commonsense knowledge (CKE) is a fundamental and challenging problem. CKE from text is known for suffering from the inherent sparsity and reporting bias of commonsense in text. Visual perception, on the other hand, contains rich commonsense knowledge about real-world entities, e.g., (person, can_hold, bottle), which can serve as promising sources for acquiring grounded commonsense knowledge. In this work, we present CLEVER, which formulates CKE as a distantly supervised multi-instance learning problem, where models learn to summarize commonsense relations from a bag of images about an entity pair without any human annotation on image instances. To address the problem, CLEVER leverages vision-language pre-training models for deep understanding of each image in the bag, and selects informative instances from the bag to summarize commonsense entity relations via a novel contrastive attention mechanism. Comprehensive experimental results in held-out and human evaluation show that CLEVER can extract commonsense knowledge in promising quality, outperforming pre-trained language model-based methods by 3.9 AUC and 6.4 mAUC points. The predicted commonsense scores show strong correlation with human judgment with a 0.78 Spearman coefficient. Moreover, the extracted commonsense can also be grounded into images with reasonable interpretability. The data and codes can be obtained at https://github.com/thunlp/CLEVER.
[ { "version": "v1", "created": "Tue, 22 Nov 2022 07:00:16 GMT" }, { "version": "v2", "created": "Sat, 25 Mar 2023 07:16:48 GMT" } ]
2023-03-28T00:00:00
[ [ "Yao", "Yuan", "" ], [ "Yu", "Tianyu", "" ], [ "Zhang", "Ao", "" ], [ "Li", "Mengdi", "" ], [ "Xie", "Ruobing", "" ], [ "Weber", "Cornelius", "" ], [ "Liu", "Zhiyuan", "" ], [ "Zheng", "Hai-Tao", "" ], [ "Wermter", "Stefan", "" ], [ "Chua", "Tat-Seng", "" ], [ "Sun", "Maosong", "" ] ]
new_dataset
0.966313
2211.17104
Arash Vaezi
Arash Vaezi
Agent-Cells with DNA Programming: A Dynamic Decentralized System
null
null
null
null
cs.MA cs.CR cs.DC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper introduces a new concept. We intend to give life to a software agent. A software agent is a computer program that acts on a user's behalf. We put a DNA inside the agent. DNA is a simple text, a whole roadmap of a network of agents or a system with details. A Dynamic Numerical Abstract of a multiagent system. It is also a reproductive part for an \emph{agent} that makes the agent take actions and decide independently and reproduce coworkers. By defining different DNA structures, one can establish new agents and different nets for different usages. We initiate such thinking as \emph{DNA programming}. This strategy leads to a new field of programming. This type of programming can help us manage large systems with various elements with an incredibly organized customizable structure. An agent can reproduce another agent. We put one or a few agents around a given network, and the agents will reproduce themselves till they can reach others and pervade the whole network. An agent's position or other environmental or geographical characteristics make it possible for an agent to know its active set of \emph{genes} on its DNA. The active set of genes specifies its duties. There is a database that includes a list of functions s.t. each one is an implementation of what a \emph{gene} represents. To utilize a decentralized database, we may use a blockchain-based structure. This design can adapt to a system that manages many static and dynamic networks. This network could be a distributed system, a decentralized system, a telecommunication network such as a 5G monitoring system, an IoT management system, or even an energy management system. The final system is the combination of all the agents and the overlay net that connects the agents. We denote the final net as the \emph{body} of the system.
[ { "version": "v1", "created": "Sun, 2 Oct 2022 16:53:49 GMT" }, { "version": "v2", "created": "Thu, 8 Dec 2022 06:10:44 GMT" }, { "version": "v3", "created": "Mon, 27 Mar 2023 12:57:49 GMT" } ]
2023-03-28T00:00:00
[ [ "Vaezi", "Arash", "" ] ]
new_dataset
0.997795
2212.00137
Abrar Alali Mrs.
Abrar Alali, Stephan Olariu, Shubham Jain
ADOPT: A system for Alerting Drivers to Occluded Pedestrian Traffic
null
null
10.1016/j.vehcom.2023.100601
null
cs.MA
http://creativecommons.org/licenses/by-nc-sa/4.0/
Recent statistics reveal an alarming increase in accidents involving pedestrians (especially children) crossing the street. A common philosophy of existing pedestrian detection approaches is that this task should be undertaken by the moving cars themselves. In sharp departure from this philosophy, we propose to enlist the help of cars parked along the sidewalk to detect and protect crossing pedestrians. In support of this goal, we propose ADOPT: a system for Alerting Drivers to Occluded Pedestrian Traffic. ADOPT lays the theoretical foundations of a system that uses parked cars to: (1) detect the presence of a group of crossing pedestrians - a crossing cohort; (2) predict the time the last member of the cohort takes to clear the street; (3) send alert messages to those approaching cars that may reach the crossing area while pedestrians are still in the street; and, (4) show how approaching cars can adjust their speed, given several simultaneous crossing locations. Importantly, in ADOPT all communications occur over very short distances and at very low power. Our extensive simulations using SUMO-generated pedestrian and car traffic have shown the effectiveness of ADOPT in detecting and protecting crossing pedestrians.
[ { "version": "v1", "created": "Thu, 20 Oct 2022 13:34:43 GMT" } ]
2023-03-28T00:00:00
[ [ "Alali", "Abrar", "" ], [ "Olariu", "Stephan", "" ], [ "Jain", "Shubham", "" ] ]
new_dataset
0.972698
2212.00452
Marc Alexa
Marc Alexa
Tutte Embeddings of Tetrahedral Meshes
null
Discrete Comput Geom (2023)
10.1007/s00454-023-00494-0
null
cs.CG cs.GR math.CO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Tutte's embedding theorem states that every 3-connected graph without a $K_5$ or $K_{3,3}$ minor (i.e. a planar graph) is embedded in the plane if the outer face is in convex position and the interior vertices are convex combinations of their neighbors. We show that this result extends to simply connected tetrahedral meshes in a natural way: for the tetrahedral mesh to be embedded if the outer polyhedron is in convex position and the interior vertices are convex combination of their neighbors it is sufficient (but not necessary) that the graph of the tetrahedral mesh contains no $K_6$ and no $K_{3,3,1}$, and all triangles incident on three boundary vertices are boundary triangles.
[ { "version": "v1", "created": "Thu, 1 Dec 2022 12:06:23 GMT" }, { "version": "v2", "created": "Tue, 7 Feb 2023 15:16:24 GMT" } ]
2023-03-28T00:00:00
[ [ "Alexa", "Marc", "" ] ]
new_dataset
0.993228
2212.01206
Norman M\"uller
Norman M\"uller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bul\`o, Peter Kontschieder, Matthias Nie{\ss}ner
DiffRF: Rendering-Guided 3D Radiance Field Diffusion
Project page: https://sirwyver.github.io/DiffRF/ Video: https://youtu.be/qETBcLu8SUk - CVPR 2023 Highlight - updated evaluations after fixing initial data mapping error on all methods
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
We introduce DiffRF, a novel approach for 3D radiance field synthesis based on denoising diffusion probabilistic models. While existing diffusion-based methods operate on images, latent codes, or point cloud data, we are the first to directly generate volumetric radiance fields. To this end, we propose a 3D denoising model which directly operates on an explicit voxel grid representation. However, as radiance fields generated from a set of posed images can be ambiguous and contain artifacts, obtaining ground truth radiance field samples is non-trivial. We address this challenge by pairing the denoising formulation with a rendering loss, enabling our model to learn a deviated prior that favours good image quality instead of trying to replicate fitting errors like floating artifacts. In contrast to 2D-diffusion models, our model learns multi-view consistent priors, enabling free-view synthesis and accurate shape generation. Compared to 3D GANs, our diffusion-based approach naturally enables conditional generation such as masked completion or single-view 3D synthesis at inference time.
[ { "version": "v1", "created": "Fri, 2 Dec 2022 14:37:20 GMT" }, { "version": "v2", "created": "Mon, 27 Mar 2023 14:51:07 GMT" } ]
2023-03-28T00:00:00
[ [ "Müller", "Norman", "" ], [ "Siddiqui", "Yawar", "" ], [ "Porzi", "Lorenzo", "" ], [ "Bulò", "Samuel Rota", "" ], [ "Kontschieder", "Peter", "" ], [ "Nießner", "Matthias", "" ] ]
new_dataset
0.997724
2301.00269
Ali Abedi Abedi
Ali Abedi, Haofan Lu, Alex Chen, Charlie Liu, Omid Abari
WiFi Physical Layer Stays Awake and Responds When it Should Not
12 pages
null
null
null
cs.NI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
WiFi communication should be possible only between devices inside the same network. However, we find that all existing WiFi devices send back acknowledgments (ACK) to even fake packets received from unauthorized WiFi devices outside of their network. Moreover, we find that an unauthorized device can manipulate the power-saving mechanism of WiFi radios and keep them continuously awake by sending specific fake beacon frames to them. Our evaluation of over 5,000 devices from 186 vendors confirms that these are widespread issues. We believe these loopholes cannot be prevented, and hence they create privacy and security concerns. Finally, to show the importance of these issues and their consequences, we implement and demonstrate two attacks where an adversary performs battery drain and WiFi sensing attacks just using a tiny WiFi module which costs less than ten dollars.
[ { "version": "v1", "created": "Sat, 31 Dec 2022 19:07:14 GMT" }, { "version": "v2", "created": "Sat, 25 Mar 2023 01:11:55 GMT" } ]
2023-03-28T00:00:00
[ [ "Abedi", "Ali", "" ], [ "Lu", "Haofan", "" ], [ "Chen", "Alex", "" ], [ "Liu", "Charlie", "" ], [ "Abari", "Omid", "" ] ]
new_dataset
0.980522
2301.06287
Kaicheng Yang
Rachith Aiyappa, Matthew R. DeVerna, Manita Pote, Bao Tran Truong, Wanying Zhao, David Axelrod, Aria Pessianzadeh, Zoher Kachwala, Munjung Kim, Ozgur Can Seckin, Minsuk Kim, Sunny Gandhi, Amrutha Manikonda, Francesco Pierri, Filippo Menczer, Kai-Cheng Yang
A Multi-Platform Collection of Social Media Posts about the 2022 U.S. Midterm Elections
8 pages, 3 figures, forthcoming in ICWSM23
null
null
null
cs.SI
http://creativecommons.org/licenses/by-nc-nd/4.0/
Social media are utilized by millions of citizens to discuss important political issues. Politicians use these platforms to connect with the public and broadcast policy positions. Therefore, data from social media has enabled many studies of political discussion. While most analyses are limited to data from individual platforms, people are embedded in a larger information ecosystem spanning multiple social networks. Here we describe and provide access to the Indiana University 2022 U.S. Midterms Multi-Platform Social Media Dataset (MEIU22), a collection of social media posts from Twitter, Facebook, Instagram, Reddit, and 4chan. MEIU22 links to posts about the midterm elections based on a comprehensive list of keywords and tracks the social media accounts of 1,011 candidates from October 1 to December 25, 2022. We also publish the source code of our pipeline to enable similar multi-platform research projects.
[ { "version": "v1", "created": "Mon, 16 Jan 2023 07:12:43 GMT" }, { "version": "v2", "created": "Mon, 27 Mar 2023 00:44:27 GMT" } ]
2023-03-28T00:00:00
[ [ "Aiyappa", "Rachith", "" ], [ "DeVerna", "Matthew R.", "" ], [ "Pote", "Manita", "" ], [ "Truong", "Bao Tran", "" ], [ "Zhao", "Wanying", "" ], [ "Axelrod", "David", "" ], [ "Pessianzadeh", "Aria", "" ], [ "Kachwala", "Zoher", "" ], [ "Kim", "Munjung", "" ], [ "Seckin", "Ozgur Can", "" ], [ "Kim", "Minsuk", "" ], [ "Gandhi", "Sunny", "" ], [ "Manikonda", "Amrutha", "" ], [ "Pierri", "Francesco", "" ], [ "Menczer", "Filippo", "" ], [ "Yang", "Kai-Cheng", "" ] ]
new_dataset
0.991257
2301.07302
Ram Ramrakhya
Ram Ramrakhya, Dhruv Batra, Erik Wijmans, Abhishek Das
PIRLNav: Pretraining with Imitation and RL Finetuning for ObjectNav
8 pages + supplement
null
null
null
cs.LG cs.AI cs.RO
http://creativecommons.org/licenses/by/4.0/
We study ObjectGoal Navigation -- where a virtual robot situated in a new environment is asked to navigate to an object. Prior work has shown that imitation learning (IL) using behavior cloning (BC) on a dataset of human demonstrations achieves promising results. However, this has limitations -- 1) BC policies generalize poorly to new states, since the training mimics actions not their consequences, and 2) collecting demonstrations is expensive. On the other hand, reinforcement learning (RL) is trivially scalable, but requires careful reward engineering to achieve desirable behavior. We present PIRLNav, a two-stage learning scheme for BC pretraining on human demonstrations followed by RL-finetuning. This leads to a policy that achieves a success rate of $65.0\%$ on ObjectNav ($+5.0\%$ absolute over previous state-of-the-art). Using this BC$\rightarrow$RL training recipe, we present a rigorous empirical analysis of design choices. First, we investigate whether human demonstrations can be replaced with `free' (automatically generated) sources of demonstrations, e.g. shortest paths (SP) or task-agnostic frontier exploration (FE) trajectories. We find that BC$\rightarrow$RL on human demonstrations outperforms BC$\rightarrow$RL on SP and FE trajectories, even when controlled for same BC-pretraining success on train, and even on a subset of val episodes where BC-pretraining success favors the SP or FE policies. Next, we study how RL-finetuning performance scales with the size of the BC pretraining dataset. We find that as we increase the size of BC-pretraining dataset and get to high BC accuracies, improvements from RL-finetuning are smaller, and that $90\%$ of the performance of our best BC$\rightarrow$RL policy can be achieved with less than half the number of BC demonstrations. Finally, we analyze failure modes of our ObjectNav policies, and present guidelines for further improving them.
[ { "version": "v1", "created": "Wed, 18 Jan 2023 04:40:50 GMT" }, { "version": "v2", "created": "Sun, 26 Mar 2023 14:49:25 GMT" } ]
2023-03-28T00:00:00
[ [ "Ramrakhya", "Ram", "" ], [ "Batra", "Dhruv", "" ], [ "Wijmans", "Erik", "" ], [ "Das", "Abhishek", "" ] ]
new_dataset
0.999735
2301.09632
Ang Cao
Ang Cao, Justin Johnson
HexPlane: A Fast Representation for Dynamic Scenes
CVPR 2023, Camera Ready Project page: https://caoang327.github.io/HexPlane
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Modeling and re-rendering dynamic 3D scenes is a challenging task in 3D vision. Prior approaches build on NeRF and rely on implicit representations. This is slow since it requires many MLP evaluations, constraining real-world applications. We show that dynamic 3D scenes can be explicitly represented by six planes of learned features, leading to an elegant solution we call HexPlane. A HexPlane computes features for points in spacetime by fusing vectors extracted from each plane, which is highly efficient. Pairing a HexPlane with a tiny MLP to regress output colors and training via volume rendering gives impressive results for novel view synthesis on dynamic scenes, matching the image quality of prior work but reducing training time by more than $100\times$. Extensive ablations confirm our HexPlane design and show that it is robust to different feature fusion mechanisms, coordinate systems, and decoding mechanisms. HexPlane is a simple and effective solution for representing 4D volumes, and we hope they can broadly contribute to modeling spacetime for dynamic 3D scenes.
[ { "version": "v1", "created": "Mon, 23 Jan 2023 18:59:25 GMT" }, { "version": "v2", "created": "Mon, 27 Mar 2023 16:39:58 GMT" } ]
2023-03-28T00:00:00
[ [ "Cao", "Ang", "" ], [ "Johnson", "Justin", "" ] ]
new_dataset
0.999404
2301.10241
Giacomo Meanti
Sara Fridovich-Keil, Giacomo Meanti, Frederik Warburg, Benjamin Recht, Angjoo Kanazawa
K-Planes: Explicit Radiance Fields in Space, Time, and Appearance
Project page https://sarafridov.github.io/K-Planes/
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions. Our model uses d choose 2 planes to represent a d-dimensional scene, providing a seamless way to go from static (d=3) to dynamic (d=4) scenes. This planar factorization makes adding dimension-specific priors easy, e.g. temporal smoothness and multi-resolution spatial structure, and induces a natural decomposition of static and dynamic components of a scene. We use a linear feature decoder with a learned color basis that yields similar performance as a nonlinear black-box MLP decoder. Across a range of synthetic and real, static and dynamic, fixed and varying appearance scenes, k-planes yields competitive and often state-of-the-art reconstruction fidelity with low memory usage, achieving 1000x compression over a full 4D grid, and fast optimization with a pure PyTorch implementation. For video results and code, please see https://sarafridov.github.io/K-Planes.
[ { "version": "v1", "created": "Tue, 24 Jan 2023 18:59:08 GMT" }, { "version": "v2", "created": "Fri, 24 Mar 2023 21:32:50 GMT" } ]
2023-03-28T00:00:00
[ [ "Fridovich-Keil", "Sara", "" ], [ "Meanti", "Giacomo", "" ], [ "Warburg", "Frederik", "" ], [ "Recht", "Benjamin", "" ], [ "Kanazawa", "Angjoo", "" ] ]
new_dataset
0.997231
2302.13056
Huasong Zhou
Huasong Zhou, Xiaowei Xu, Xiaodong Wang, and Leon Bevan Bullock
SATBA: An Invisible Backdoor Attack Based On Spatial Attention
15 pages, 6 figures
null
null
null
cs.CR cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Backdoor attacks pose a new and emerging threat to AI security, where Deep Neural Networks (DNNs) are trained on datasets added to hidden trigger patterns. Although the poisoned model behaves normally on benign samples, it produces anomalous results on samples containing the trigger pattern. Nevertheless, most existing backdoor attacks face two significant drawbacks: their trigger patterns are visible and easy to detect by human inspection, and their injection process leads to the loss of natural sample features and trigger patterns, thereby reducing the attack success rate and the model accuracy. In this paper, we propose a novel backdoor attack named SATBA that overcomes these limitations by using spatial attention mechanism and U-type model. Our attack leverages spatial attention mechanism to extract data features and generate invisible trigger patterns that are correlated with clean data. Then it uses U-type model to plant these trigger patterns into the original data without causing noticeable feature loss. We evaluate our attack on three prominent image classification DNNs across three standard datasets and demonstrate that it achieves high attack success rate and robustness against backdoor defenses. Additionally, we also conduct extensive experiments on image similarity to highlight the stealthiness of our attack.
[ { "version": "v1", "created": "Sat, 25 Feb 2023 10:57:41 GMT" }, { "version": "v2", "created": "Sun, 26 Mar 2023 14:23:10 GMT" } ]
2023-03-28T00:00:00
[ [ "Zhou", "Huasong", "" ], [ "Xu", "Xiaowei", "" ], [ "Wang", "Xiaodong", "" ], [ "Bullock", "Leon Bevan", "" ] ]
new_dataset
0.977253
2302.13543
Sameera Ramasinghe Mr.
Sameera Ramasinghe, Violetta Shevchenko, Gil Avraham, Anton Van Den Hengel
BLiRF: Bandlimited Radiance Fields for Dynamic Scene Modeling
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Reasoning the 3D structure of a non-rigid dynamic scene from a single moving camera is an under-constrained problem. Inspired by the remarkable progress of neural radiance fields (NeRFs) in photo-realistic novel view synthesis of static scenes, extensions have been proposed for dynamic settings. These methods heavily rely on neural priors in order to regularize the problem. In this work, we take a step back and reinvestigate how current implementations may entail deleterious effects, including limited expressiveness, entanglement of light and density fields, and sub-optimal motion localization. As a remedy, we advocate for a bridge between classic non-rigid-structure-from-motion (\nrsfm) and NeRF, enabling the well-studied priors of the former to constrain the latter. To this end, we propose a framework that factorizes time and space by formulating a scene as a composition of bandlimited, high-dimensional signals. We demonstrate compelling results across complex dynamic scenes that involve changes in lighting, texture and long-range dynamics.
[ { "version": "v1", "created": "Mon, 27 Feb 2023 06:40:32 GMT" }, { "version": "v2", "created": "Sat, 18 Mar 2023 10:10:40 GMT" }, { "version": "v3", "created": "Sat, 25 Mar 2023 02:18:02 GMT" } ]
2023-03-28T00:00:00
[ [ "Ramasinghe", "Sameera", "" ], [ "Shevchenko", "Violetta", "" ], [ "Avraham", "Gil", "" ], [ "Hengel", "Anton Van Den", "" ] ]
new_dataset
0.969086
2303.00938
Jialiang Zhang
Yinzhen Xu, Weikang Wan, Jialiang Zhang, Haoran Liu, Zikang Shan, Hao Shen, Ruicheng Wang, Haoran Geng, Yijia Weng, Jiayi Chen, Tengyu Liu, Li Yi, He Wang
UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy
Accepted to CVPR 2023
null
null
null
cs.RO cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up objects in high-quality and diverse ways and generalize across hundreds of categories and even the unseen. Inspired by successful pipelines used in parallel gripper grasping, we split the task into two stages: 1) grasp proposal (pose) generation and 2) goal-conditioned grasp execution. For the first stage, we propose a novel probabilistic model of grasp pose conditioned on the point cloud observation that factorizes rotation from translation and articulation. Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud.For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution. Note that it is very challenging to learn this highly generalizable grasp policy that only takes realistic inputs without oracle states. We thus propose several important innovations, including state canonicalization, object curriculum, and teacher-student distillation. Integrating the two stages, our final pipeline becomes the first to achieve universal generalization for dexterous grasping, demonstrating an average success rate of more than 60\% on thousands of object instances, which significantly outperforms all baselines, meanwhile showing only a minimal generalization gap.
[ { "version": "v1", "created": "Thu, 2 Mar 2023 03:23:18 GMT" }, { "version": "v2", "created": "Sat, 25 Mar 2023 07:35:32 GMT" } ]
2023-03-28T00:00:00
[ [ "Xu", "Yinzhen", "" ], [ "Wan", "Weikang", "" ], [ "Zhang", "Jialiang", "" ], [ "Liu", "Haoran", "" ], [ "Shan", "Zikang", "" ], [ "Shen", "Hao", "" ], [ "Wang", "Ruicheng", "" ], [ "Geng", "Haoran", "" ], [ "Weng", "Yijia", "" ], [ "Chen", "Jiayi", "" ], [ "Liu", "Tengyu", "" ], [ "Yi", "Li", "" ], [ "Wang", "He", "" ] ]
new_dataset
0.99726
2303.06464
Gemma Canet Tarr\'es
Gemma Canet Tarr\'es, Dan Ruta, Tu Bui, John Collomosse
PARASOL: Parametric Style Control for Diffusion Image Synthesis
Added Appendix
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We propose PARASOL, a multi-modal synthesis model that enables disentangled, parametric control of the visual style of the image by jointly conditioning synthesis on both content and a fine-grained visual style embedding. We train a latent diffusion model (LDM) using specific losses for each modality and adapt the classifier-free guidance for encouraging disentangled control over independent content and style modalities at inference time. We leverage auxiliary semantic and style-based search to create training triplets for supervision of the LDM, ensuring complementarity of content and style cues. PARASOL shows promise for enabling nuanced control over visual style in diffusion models for image creation and stylization, as well as generative search where text-based search results may be adapted to more closely match user intent by interpolating both content and style descriptors.
[ { "version": "v1", "created": "Sat, 11 Mar 2023 17:30:36 GMT" }, { "version": "v2", "created": "Mon, 27 Mar 2023 17:39:05 GMT" } ]
2023-03-28T00:00:00
[ [ "Tarrés", "Gemma Canet", "" ], [ "Ruta", "Dan", "" ], [ "Bui", "Tu", "" ], [ "Collomosse", "John", "" ] ]
new_dataset
0.998433
2303.07522
Chenguang Huang
Chenguang Huang, Oier Mees, Andy Zeng, Wolfram Burgard
Audio Visual Language Maps for Robot Navigation
Project page: https://avlmaps.github.io/
null
null
null
cs.RO cs.AI cs.CL cs.CV cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
While interacting in the world is a multi-sensory experience, many robots continue to predominantly rely on visual perception to map and navigate in their environments. In this work, we propose Audio-Visual-Language Maps (AVLMaps), a unified 3D spatial map representation for storing cross-modal information from audio, visual, and language cues. AVLMaps integrate the open-vocabulary capabilities of multimodal foundation models pre-trained on Internet-scale data by fusing their features into a centralized 3D voxel grid. In the context of navigation, we show that AVLMaps enable robot systems to index goals in the map based on multimodal queries, e.g., textual descriptions, images, or audio snippets of landmarks. In particular, the addition of audio information enables robots to more reliably disambiguate goal locations. Extensive experiments in simulation show that AVLMaps enable zero-shot multimodal goal navigation from multimodal prompts and provide 50% better recall in ambiguous scenarios. These capabilities extend to mobile robots in the real world - navigating to landmarks referring to visual, audio, and spatial concepts. Videos and code are available at: https://avlmaps.github.io.
[ { "version": "v1", "created": "Mon, 13 Mar 2023 23:17:51 GMT" }, { "version": "v2", "created": "Mon, 27 Mar 2023 15:10:51 GMT" } ]
2023-03-28T00:00:00
[ [ "Huang", "Chenguang", "" ], [ "Mees", "Oier", "" ], [ "Zeng", "Andy", "" ], [ "Burgard", "Wolfram", "" ] ]
new_dataset
0.996055
2303.10204
Michael Howard P.Eng
Michael Howard, R. Bruce Irvin
ESP32: QEMU Emulation within a Docker Container
7 pages and 9 figures
null
null
null
cs.OS
http://creativecommons.org/licenses/by/4.0/
The ESP32 is a popular microcontroller from Espressif that can be used in many embedded applications. Robotic joints, smart car chargers, beer vat agitators and automated bread mixers are a few examples where this system-on-a-chip excels. It is cheap to buy and has a number of vendors providing low-cost development board kits that come with the microcontroller and many external connection points with peripherals. There is a large software ecosystem for the ESP32. Espressif maintains an SDK containing many C-language sample projects providing a starting point for a huge variety of software services and I/O needs. Third party projects provide additional sample code as well as support for other programming languages. For example, MicroPython is a mature project with sample code and officially supported by Espressif. The SDK provides tools to not just build an application but also merge a flash image, flash to the microcontroller and monitor the output. Is it possible to build the ESP32 load and emulate on another host OS? This paper explores the QEMU emulator and its ability to emulate the ethernet interface for the guest OS. Additionally, we look into the concept of containerizing the entire emulator and ESP32 load package such that a microcontroller flash image can successfully run with a one-step deployment of a Docker container.
[ { "version": "v1", "created": "Fri, 17 Mar 2023 18:48:50 GMT" }, { "version": "v2", "created": "Sat, 25 Mar 2023 22:22:19 GMT" } ]
2023-03-28T00:00:00
[ [ "Howard", "Michael", "" ], [ "Irvin", "R. Bruce", "" ] ]
new_dataset
0.998614
2303.10826
Jiawen Zhu
Jiawen Zhu, Simiao Lai, Xin Chen, Dong Wang, Huchuan Lu
Visual Prompt Multi-Modal Tracking
Accepted by CVPR2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Visible-modal object tracking gives rise to a series of downstream multi-modal tracking tributaries. To inherit the powerful representations of the foundation model, a natural modus operandi for multi-modal tracking is full fine-tuning on the RGB-based parameters. Albeit effective, this manner is not optimal due to the scarcity of downstream data and poor transferability, etc. In this paper, inspired by the recent success of the prompt learning in language models, we develop Visual Prompt multi-modal Tracking (ViPT), which learns the modal-relevant prompts to adapt the frozen pre-trained foundation model to various downstream multimodal tracking tasks. ViPT finds a better way to stimulate the knowledge of the RGB-based model that is pre-trained at scale, meanwhile only introducing a few trainable parameters (less than 1% of model parameters). ViPT outperforms the full fine-tuning paradigm on multiple downstream tracking tasks including RGB+Depth, RGB+Thermal, and RGB+Event tracking. Extensive experiments show the potential of visual prompt learning for multi-modal tracking, and ViPT can achieve state-of-the-art performance while satisfying parameter efficiency. Code and models are available at https://github.com/jiawen-zhu/ViPT.
[ { "version": "v1", "created": "Mon, 20 Mar 2023 01:51:07 GMT" }, { "version": "v2", "created": "Sat, 25 Mar 2023 02:29:48 GMT" } ]
2023-03-28T00:00:00
[ [ "Zhu", "Jiawen", "" ], [ "Lai", "Simiao", "" ], [ "Chen", "Xin", "" ], [ "Wang", "Dong", "" ], [ "Lu", "Huchuan", "" ] ]
new_dataset
0.998209
2303.11694
Shi Linfeng
Linfeng Shi, Yan Li, Xi Zhu
Anchor Free remote sensing detector based on solving discrete polar coordinate equation
20 pages,15 figures
null
null
null
cs.CV cs.GR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
As the rapid development of depth learning, object detection in aviatic remote sensing images has become increasingly popular in recent years. Most of the current Anchor Free detectors based on key point detection sampling directly regression and classification features, with the design of object loss function based on the horizontal bounding box. It is more challenging for complex and diverse aviatic remote sensing object. In this paper, we propose an Anchor Free aviatic remote sensing object detector (BWP-Det) to detect rotating and multi-scale object. Specifically, we design a interactive double-branch(IDB) up-sampling network, in which one branch gradually up-sampling is used for the prediction of Heatmap, and the other branch is used for the regression of boundary box parameters. We improve a weighted multi-scale convolution (WmConv) in order to highlight the difference between foreground and background. We extracted Pixel level attention features from the middle layer to guide the two branches to pay attention to effective object information in the sampling process. Finally, referring to the calculation idea of horizontal IoU, we design a rotating IoU based on the split polar coordinate plane, namely JIoU, which is expressed as the intersection ratio following discretization of the inner ellipse of the rotating bounding box, to solve the correlation between angle and side length in the regression process of the rotating bounding box. Ultimately, BWP-Det, our experiments on DOTA, UCAS-AOD and NWPU VHR-10 datasets show, achieves advanced performance with simpler models and fewer regression parameters.
[ { "version": "v1", "created": "Tue, 21 Mar 2023 09:28:47 GMT" }, { "version": "v2", "created": "Sat, 25 Mar 2023 06:43:43 GMT" } ]
2023-03-28T00:00:00
[ [ "Shi", "Linfeng", "" ], [ "Li", "Yan", "" ], [ "Zhu", "Xi", "" ] ]
new_dataset
0.994783
2303.11921
Dingkang Yang
Dingkang Yang, Zhaoyu Chen, Yuzheng Wang, Shunli Wang, Mingcheng Li, Siao Liu, Xiao Zhao, Shuai Huang, Zhiyan Dong, Peng Zhai, Lihua Zhang
Context De-confounded Emotion Recognition
Accepted by CVPR 2023. CCIM is available at https://github.com/ydk122024/CCIM
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Context-Aware Emotion Recognition (CAER) is a crucial and challenging task that aims to perceive the emotional states of the target person with contextual information. Recent approaches invariably focus on designing sophisticated architectures or mechanisms to extract seemingly meaningful representations from subjects and contexts. However, a long-overlooked issue is that a context bias in existing datasets leads to a significantly unbalanced distribution of emotional states among different context scenarios. Concretely, the harmful bias is a confounder that misleads existing models to learn spurious correlations based on conventional likelihood estimation, significantly limiting the models' performance. To tackle the issue, this paper provides a causality-based perspective to disentangle the models from the impact of such bias, and formulate the causalities among variables in the CAER task via a tailored causal graph. Then, we propose a Contextual Causal Intervention Module (CCIM) based on the backdoor adjustment to de-confound the confounder and exploit the true causal effect for model training. CCIM is plug-in and model-agnostic, which improves diverse state-of-the-art approaches by considerable margins. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our CCIM and the significance of causal insight.
[ { "version": "v1", "created": "Tue, 21 Mar 2023 15:12:20 GMT" }, { "version": "v2", "created": "Sun, 26 Mar 2023 07:18:05 GMT" } ]
2023-03-28T00:00:00
[ [ "Yang", "Dingkang", "" ], [ "Chen", "Zhaoyu", "" ], [ "Wang", "Yuzheng", "" ], [ "Wang", "Shunli", "" ], [ "Li", "Mingcheng", "" ], [ "Liu", "Siao", "" ], [ "Zhao", "Xiao", "" ], [ "Huang", "Shuai", "" ], [ "Dong", "Zhiyan", "" ], [ "Zhai", "Peng", "" ], [ "Zhang", "Lihua", "" ] ]
new_dataset
0.950923
2303.12337
Tuong Do Khanh Long
Nhat Le, Thang Pham, Tuong Do, Erman Tjiputra, Quang D. Tran, Anh Nguyen
Music-Driven Group Choreography
accepted in CVPR 2023
null
null
null
cs.MM cs.CV cs.SD eess.AS
http://creativecommons.org/licenses/by/4.0/
Music-driven choreography is a challenging problem with a wide variety of industrial applications. Recently, many methods have been proposed to synthesize dance motions from music for a single dancer. However, generating dance motion for a group remains an open problem. In this paper, we present $\rm AIOZ-GDANCE$, a new large-scale dataset for music-driven group dance generation. Unlike existing datasets that only support single dance, our new dataset contains group dance videos, hence supporting the study of group choreography. We propose a semi-autonomous labeling method with humans in the loop to obtain the 3D ground truth for our dataset. The proposed dataset consists of 16.7 hours of paired music and 3D motion from in-the-wild videos, covering 7 dance styles and 16 music genres. We show that naively applying single dance generation technique to creating group dance motion may lead to unsatisfactory results, such as inconsistent movements and collisions between dancers. Based on our new dataset, we propose a new method that takes an input music sequence and a set of 3D positions of dancers to efficiently produce multiple group-coherent choreographies. We propose new evaluation metrics for measuring group dance quality and perform intensive experiments to demonstrate the effectiveness of our method. Our project facilitates future research on group dance generation and is available at: https://aioz-ai.github.io/AIOZ-GDANCE/
[ { "version": "v1", "created": "Wed, 22 Mar 2023 06:26:56 GMT" }, { "version": "v2", "created": "Mon, 27 Mar 2023 01:59:41 GMT" } ]
2023-03-28T00:00:00
[ [ "Le", "Nhat", "" ], [ "Pham", "Thang", "" ], [ "Do", "Tuong", "" ], [ "Tjiputra", "Erman", "" ], [ "Tran", "Quang D.", "" ], [ "Nguyen", "Anh", "" ] ]
new_dataset
0.999842
2303.12368
JunYong Choi
JunYong Choi and SeokYeong Lee and Haesol Park and Seung-Won Jung and Ig-Jae Kim and Junghyun Cho
MAIR: Multi-view Attention Inverse Rendering with 3D Spatially-Varying Lighting Estimation
Accepted by CVPR 2023; Project Page is https://bring728.github.io/mair.project/
null
null
null
cs.CV cs.GR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We propose a scene-level inverse rendering framework that uses multi-view images to decompose the scene into geometry, a SVBRDF, and 3D spatially-varying lighting. Because multi-view images provide a variety of information about the scene, multi-view images in object-level inverse rendering have been taken for granted. However, owing to the absence of multi-view HDR synthetic dataset, scene-level inverse rendering has mainly been studied using single-view image. We were able to successfully perform scene-level inverse rendering using multi-view images by expanding OpenRooms dataset and designing efficient pipelines to handle multi-view images, and splitting spatially-varying lighting. Our experiments show that the proposed method not only achieves better performance than single-view-based methods, but also achieves robust performance on unseen real-world scene. Also, our sophisticated 3D spatially-varying lighting volume allows for photorealistic object insertion in any 3D location.
[ { "version": "v1", "created": "Wed, 22 Mar 2023 08:07:28 GMT" }, { "version": "v2", "created": "Mon, 27 Mar 2023 04:32:11 GMT" } ]
2023-03-28T00:00:00
[ [ "Choi", "JunYong", "" ], [ "Lee", "SeokYeong", "" ], [ "Park", "Haesol", "" ], [ "Jung", "Seung-Won", "" ], [ "Kim", "Ig-Jae", "" ], [ "Cho", "Junghyun", "" ] ]
new_dataset
0.999455
2303.13277
Chong Bao
Chong Bao, Yinda Zhang, Bangbang Yang, Tianxing Fan, Zesong Yang, Hujun Bao, Guofeng Zhang and Zhaopeng Cui
SINE: Semantic-driven Image-based NeRF Editing with Prior-guided Editing Field
Accepted to CVPR 2023. Project Page: https://zju3dv.github.io/sine/
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Despite the great success in 2D editing using user-friendly tools, such as Photoshop, semantic strokes, or even text prompts, similar capabilities in 3D areas are still limited, either relying on 3D modeling skills or allowing editing within only a few categories. In this paper, we present a novel semantic-driven NeRF editing approach, which enables users to edit a neural radiance field with a single image, and faithfully delivers edited novel views with high fidelity and multi-view consistency. To achieve this goal, we propose a prior-guided editing field to encode fine-grained geometric and texture editing in 3D space, and develop a series of techniques to aid the editing process, including cyclic constraints with a proxy mesh to facilitate geometric supervision, a color compositing mechanism to stabilize semantic-driven texture editing, and a feature-cluster-based regularization to preserve the irrelevant content unchanged. Extensive experiments and editing examples on both real-world and synthetic data demonstrate that our method achieves photo-realistic 3D editing using only a single edited image, pushing the bound of semantic-driven editing in 3D real-world scenes. Our project webpage: https://zju3dv.github.io/sine/.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 13:58:11 GMT" }, { "version": "v2", "created": "Sat, 25 Mar 2023 14:58:22 GMT" } ]
2023-03-28T00:00:00
[ [ "Bao", "Chong", "" ], [ "Zhang", "Yinda", "" ], [ "Yang", "Bangbang", "" ], [ "Fan", "Tianxing", "" ], [ "Yang", "Zesong", "" ], [ "Bao", "Hujun", "" ], [ "Zhang", "Guofeng", "" ], [ "Cui", "Zhaopeng", "" ] ]
new_dataset
0.992933
2303.13992
Wenqing Li
Wenqing Li, Yue Wang, Muhammad Shafique, Saif Eddin Jabari
Physical Backdoor Trigger Activation of Autonomous Vehicle using Reachability Analysis
null
null
null
null
cs.CR cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recent studies reveal that Autonomous Vehicles (AVs) can be manipulated by hidden backdoors, causing them to perform harmful actions when activated by physical triggers. However, it is still unclear how these triggers can be activated while adhering to traffic principles. Understanding this vulnerability in a dynamic traffic environment is crucial. This work addresses this gap by presenting physical trigger activation as a reachability problem of controlled dynamic system. Our technique identifies security-critical areas in traffic systems where trigger conditions for accidents can be reached, and provides intended trajectories for how those conditions can be reached. Testing on typical traffic scenarios showed the system can be successfully driven to trigger conditions with near 100% activation rate. Our method benefits from identifying AV vulnerability and enabling effective safety strategies.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 13:35:55 GMT" }, { "version": "v2", "created": "Mon, 27 Mar 2023 04:05:48 GMT" } ]
2023-03-28T00:00:00
[ [ "Li", "Wenqing", "" ], [ "Wang", "Yue", "" ], [ "Shafique", "Muhammad", "" ], [ "Jabari", "Saif Eddin", "" ] ]
new_dataset
0.961033
2303.14092
Haiyu Zhang
Mingwu Zheng, Haiyu Zhang, Hongyu Yang, Di Huang
NeuFace: Realistic 3D Neural Face Rendering from Multi-view Images
Accepted to CVPR 2023, code is released at https://github.com/aejion/NeuFace
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Realistic face rendering from multi-view images is beneficial to various computer vision and graphics applications. Due to the complex spatially-varying reflectance properties and geometry characteristics of faces, however, it remains challenging to recover 3D facial representations both faithfully and efficiently in the current studies. This paper presents a novel 3D face rendering model, namely NeuFace, to learn accurate and physically-meaningful underlying 3D representations by neural rendering techniques. It naturally incorporates the neural BRDFs into physically based rendering, capturing sophisticated facial geometry and appearance clues in a collaborative manner. Specifically, we introduce an approximated BRDF integration and a simple yet new low-rank prior, which effectively lower the ambiguities and boost the performance of the facial BRDFs. Extensive experiments demonstrate the superiority of NeuFace in human face rendering, along with a decent generalization ability to common objects.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 15:57:39 GMT" }, { "version": "v2", "created": "Mon, 27 Mar 2023 05:17:02 GMT" } ]
2023-03-28T00:00:00
[ [ "Zheng", "Mingwu", "" ], [ "Zhang", "Haiyu", "" ], [ "Yang", "Hongyu", "" ], [ "Huang", "Di", "" ] ]
new_dataset
0.999007
2303.14207
Jiapeng Tang
Jiapeng Tang, Yinyu Nie, Lev Markhasin, Angela Dai, Justus Thies, Matthias Nie{\ss}ner
DiffuScene: Scene Graph Denoising Diffusion Probabilistic Model for Generative Indoor Scene Synthesis
13 figures, 5 tables
null
null
null
cs.CV
http://creativecommons.org/licenses/by-sa/4.0/
We present DiffuScene for indoor 3D scene synthesis based on a novel scene graph denoising diffusion probabilistic model, which generates 3D instance properties stored in a fully-connected scene graph and then retrieves the most similar object geometry for each graph node i.e. object instance which is characterized as a concatenation of different attributes, including location, size, orientation, semantic, and geometry features. Based on this scene graph, we designed a diffusion model to determine the placements and types of 3D instances. Our method can facilitate many downstream applications, including scene completion, scene arrangement, and text-conditioned scene synthesis. Experiments on the 3D-FRONT dataset show that our method can synthesize more physically plausible and diverse indoor scenes than state-of-the-art methods. Extensive ablation studies verify the effectiveness of our design choice in scene diffusion models.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 18:00:15 GMT" } ]
2023-03-28T00:00:00
[ [ "Tang", "Jiapeng", "" ], [ "Nie", "Yinyu", "" ], [ "Markhasin", "Lev", "" ], [ "Dai", "Angela", "" ], [ "Thies", "Justus", "" ], [ "Nießner", "Matthias", "" ] ]
new_dataset
0.990592
2303.14301
Michael Zellinger
Michael J. Zellinger and Peter B\"uhlmann
repliclust: Synthetic Data for Cluster Analysis
21 pages, 11 figures
null
null
null
cs.LG stat.CO stat.ML
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present repliclust (from repli-cate and clust-er), a Python package for generating synthetic data sets with clusters. Our approach is based on data set archetypes, high-level geometric descriptions from which the user can create many different data sets, each possessing the desired geometric characteristics. The architecture of our software is modular and object-oriented, decomposing data generation into algorithms for placing cluster centers, sampling cluster shapes, selecting the number of data points for each cluster, and assigning probability distributions to clusters. The project webpage, repliclust.org, provides a concise user guide and thorough documentation.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 23:45:27 GMT" } ]
2023-03-28T00:00:00
[ [ "Zellinger", "Michael J.", "" ], [ "Bühlmann", "Peter", "" ] ]
new_dataset
0.969476
2303.14310
Zhen Wang
Ana Jojic, Zhen Wang, Nebojsa Jojic
GPT is becoming a Turing machine: Here are some ways to program it
25 pages, 1 figure
null
null
null
cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We demonstrate that, through appropriate prompting, GPT-3 family of models can be triggered to perform iterative behaviours necessary to execute (rather than just write or recall) programs that involve loops, including several popular algorithms found in computer science curricula or software developer interviews. We trigger execution and description of Iterations by Regimenting Self-Attention (IRSA) in one (or a combination) of three ways: 1) Using strong repetitive structure in an example of an execution path of a target program for one particular input, 2) Prompting with fragments of execution paths, and 3) Explicitly forbidding (skipping) self-attention to parts of the generated text. On a dynamic program execution, IRSA leads to larger accuracy gains than replacing the model with the much more powerful GPT-4. IRSA has promising applications in education, as the prompts and responses resemble student assignments in data structures and algorithms classes. Our findings hold implications for evaluating LLMs, which typically target the in-context learning: We show that prompts that may not even cover one full task example can trigger algorithmic behaviour, allowing solving problems previously thought of as hard for LLMs, such as logical puzzles. Consequently, prompt design plays an even more critical role in LLM performance than previously recognized.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 00:43:41 GMT" } ]
2023-03-28T00:00:00
[ [ "Jojic", "Ana", "" ], [ "Wang", "Zhen", "" ], [ "Jojic", "Nebojsa", "" ] ]
new_dataset
0.994226
2303.14377
Chenchen Xu
Chenchen Xu and Min Zhou and Tiezheng Ge and Yuning Jiang and Weiwei Xu
Unsupervised Domain Adaption with Pixel-level Discriminator for Image-aware Layout Generation
8 pages, 4 figures, 7 tables, accepted by CVPR2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Layout is essential for graphic design and poster generation. Recently, applying deep learning models to generate layouts has attracted increasing attention. This paper focuses on using the GAN-based model conditioned on image contents to generate advertising poster graphic layouts, which requires an advertising poster layout dataset with paired product images and graphic layouts. However, the paired images and layouts in the existing dataset are collected by inpainting and annotating posters, respectively. There exists a domain gap between inpainted posters (source domain data) and clean product images (target domain data). Therefore, this paper combines unsupervised domain adaption techniques to design a GAN with a novel pixel-level discriminator (PD), called PDA-GAN, to generate graphic layouts according to image contents. The PD is connected to the shallow level feature map and computes the GAN loss for each input-image pixel. Both quantitative and qualitative evaluations demonstrate that PDA-GAN can achieve state-of-the-art performances and generate high-quality image-aware graphic layouts for advertising posters.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 06:50:22 GMT" } ]
2023-03-28T00:00:00
[ [ "Xu", "Chenchen", "" ], [ "Zhou", "Min", "" ], [ "Ge", "Tiezheng", "" ], [ "Jiang", "Yuning", "" ], [ "Xu", "Weiwei", "" ] ]
new_dataset
0.999177
2303.14386
Hwanjun Song
Hwanjun Song, Jihwan Bang
Prompt-Guided Transformers for End-to-End Open-Vocabulary Object Detection
version 1
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Prompt-OVD is an efficient and effective framework for open-vocabulary object detection that utilizes class embeddings from CLIP as prompts, guiding the Transformer decoder to detect objects in both base and novel classes. Additionally, our novel RoI-based masked attention and RoI pruning techniques help leverage the zero-shot classification ability of the Vision Transformer-based CLIP, resulting in improved detection performance at minimal computational cost. Our experiments on the OV-COCO and OVLVIS datasets demonstrate that Prompt-OVD achieves an impressive 21.2 times faster inference speed than the first end-to-end open-vocabulary detection method (OV-DETR), while also achieving higher APs than four two-stage-based methods operating within similar inference time ranges. Code will be made available soon.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 07:31:08 GMT" } ]
2023-03-28T00:00:00
[ [ "Song", "Hwanjun", "" ], [ "Bang", "Jihwan", "" ] ]
new_dataset
0.980996
2303.14407
Yiqian Wu
Yiqian Wu, Jing Zhang, Hongbo Fu, Xiaogang Jin
LPFF: A Portrait Dataset for Face Generators Across Large Poses
9 figures
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The creation of 2D realistic facial images and 3D face shapes using generative networks has been a hot topic in recent years. Existing face generators exhibit exceptional performance on faces in small to medium poses (with respect to frontal faces) but struggle to produce realistic results for large poses. The distorted rendering results on large poses in 3D-aware generators further show that the generated 3D face shapes are far from the distribution of 3D faces in reality. We find that the above issues are caused by the training dataset's pose imbalance. In this paper, we present LPFF, a large-pose Flickr face dataset comprised of 19,590 high-quality real large-pose portrait images. We utilize our dataset to train a 2D face generator that can process large-pose face images, as well as a 3D-aware generator that can generate realistic human face geometry. To better validate our pose-conditional 3D-aware generators, we develop a new FID measure to evaluate the 3D-level performance. Through this novel FID measure and other experiments, we show that LPFF can help 2D face generators extend their latent space and better manipulate the large-pose data, and help 3D-aware face generators achieve better view consistency and more realistic 3D reconstruction results.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 09:07:36 GMT" } ]
2023-03-28T00:00:00
[ [ "Wu", "Yiqian", "" ], [ "Zhang", "Jing", "" ], [ "Fu", "Hongbo", "" ], [ "Jin", "Xiaogang", "" ] ]
new_dataset
0.999847
2303.14408
Ziqin Wang
Ziqin Wang, Bowen Cheng, Lichen Zhao, Dong Xu, Yang Tang, Lu Sheng
VL-SAT: Visual-Linguistic Semantics Assisted Training for 3D Semantic Scene Graph Prediction in Point Cloud
CVPR2023 Highlight
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The task of 3D semantic scene graph (3DSSG) prediction in the point cloud is challenging since (1) the 3D point cloud only captures geometric structures with limited semantics compared to 2D images, and (2) long-tailed relation distribution inherently hinders the learning of unbiased prediction. Since 2D images provide rich semantics and scene graphs are in nature coped with languages, in this study, we propose Visual-Linguistic Semantics Assisted Training (VL-SAT) scheme that can significantly empower 3DSSG prediction models with discrimination about long-tailed and ambiguous semantic relations. The key idea is to train a powerful multi-modal oracle model to assist the 3D model. This oracle learns reliable structural representations based on semantics from vision, language, and 3D geometry, and its benefits can be heterogeneously passed to the 3D model during the training stage. By effectively utilizing visual-linguistic semantics in training, our VL-SAT can significantly boost common 3DSSG prediction models, such as SGFN and SGGpoint, only with 3D inputs in the inference stage, especially when dealing with tail relation triplets. Comprehensive evaluations and ablation studies on the 3DSSG dataset have validated the effectiveness of the proposed scheme. Code is available at https://github.com/wz7in/CVPR2023-VLSAT.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 09:14:18 GMT" } ]
2023-03-28T00:00:00
[ [ "Wang", "Ziqin", "" ], [ "Cheng", "Bowen", "" ], [ "Zhao", "Lichen", "" ], [ "Xu", "Dong", "" ], [ "Tang", "Yang", "" ], [ "Sheng", "Lu", "" ] ]
new_dataset
0.994565
2303.14425
Zhouhong Gu
Zhouhong Gu, Sihang Jiang, Wenhao Huang, Jiaqing Liang, Hongwei Feng, Yanghua Xiao
Sem4SAP: Synonymous Expression Mining From Open Knowledge Graph For Language Model Synonym-Aware Pretraining
null
null
null
null
cs.CL cs.AI
http://creativecommons.org/licenses/by/4.0/
The model's ability to understand synonymous expression is crucial in many kinds of downstream tasks. It will make the model to better understand the similarity between context, and more robust to the synonym substitution attack. However, many Pretrained Language Model (PLM) lack synonym knowledge due to limitation of small-scale synsets and PLM's pretraining objectives. In this paper, we propose a framework called Sem4SAP to mine synsets from Open Knowledge Graph (Open-KG) and using the mined synsets to do synonym-aware pretraining for language models. We propose to coarsly filter the content in Open-KG and use the frequency information to better help the clustering process under low-resource unsupervised conditions. We expand the mined synsets by migrating core semantics between synonymous expressions.We also propose two novel and effective synonym-aware pre-training methods for injecting synonym knowledge into PLMs.Extensive experiments demonstrate that Sem4SAP can dramatically outperform the original PLMs and other baselines on ten different tasks.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 10:19:14 GMT" } ]
2023-03-28T00:00:00
[ [ "Gu", "Zhouhong", "" ], [ "Jiang", "Sihang", "" ], [ "Huang", "Wenhao", "" ], [ "Liang", "Jiaqing", "" ], [ "Feng", "Hongwei", "" ], [ "Xiao", "Yanghua", "" ] ]
new_dataset
0.973874
2303.14436
Topside Mathonsi
Beauty L. Komane and Topside E. Mathonsi
Design of a Smart Waste Management System for the City of Johannesburg
null
null
null
null
cs.GT cs.AI
http://creativecommons.org/publicdomain/zero/1.0/
Every human being in this world produces waste. South Africa is a developing country with many townships that have limited waste resources. Over-increasing population growth overpowers the volume of most municipal authorities to provide even the most essential services. Waste in townships is produced via littering, dumping of bins, cutting of trees, dumping of waste near rivers, and overrunning of waste bins. Waste increases diseases, air pollution, and environmental pollution, and lastly increases gas emissions that contribute to the release of greenhouse gases. The ungathered waste is dumped widely in the streets and drains contributing to flooding, breeding of insects, rodent vectors, and spreading of diseases. Therefore, the aim of this paper is to design a smart waste management system for the city of Johannesburg. The city of Johannesburg contains waste municipality workers and has provided some areas with waste resources such as waste bins and trucks for collecting waste. But the problem is that the resources only are not enough to solve the problem of waste in the city. The waste municipality uses traditional ways of collecting waste such as going to each street and picking up waste bins. The traditional way has worked for years but as the population is increasing more waste is produced which causes various problems for the waste municipalities and the public at large. The proposed system consists of sensors, user applications, and a real-time monitoring system. This paper adopts the experimental methodology.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 11:14:58 GMT" } ]
2023-03-28T00:00:00
[ [ "Komane", "Beauty L.", "" ], [ "Mathonsi", "Topside E.", "" ] ]
new_dataset
0.951442
2303.14438
Javier Ron
Javier Ron, C\'esar Soto-Valero, Long Zhang, Benoit Baudry, Martin Monperrus
Highly Available Blockchain Nodes With N-Version Design
null
null
null
null
cs.SE
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
As all software, blockchain nodes are exposed to faults in their underlying execution stack. Unstable execution environments can disrupt the availability of blockchain nodes interfaces, resulting in downtime for users. This paper introduces the concept of N-version Blockchain nodes. This new type of node relies on simultaneous execution of different implementations of the same blockchain protocol, in the line of Avizienis' N-version programming vision. We design and implement an N-version blockchain node prototype in the context of Ethereum, called N-ETH. We show that N-ETH is able to mitigate the effects of unstable execution environments and significantly enhance availability under environment faults. To simulate unstable execution environments, we perform fault injection at the system-call level. Our results show that existing Ethereum node implementations behave asymmetrically under identical instability scenarios. N-ETH leverages this asymmetric behavior available in the diverse implementations of Ethereum nodes to provide increased availability, even under our most aggressive fault-injection strategies. We are the first to validate the relevance of N-version design in the domain of blockchain infrastructure. From an industrial perspective, our results are of utmost importance for businesses operating blockchain nodes, including Google, ConsenSys, and many other major blockchain companies.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 11:16:17 GMT" } ]
2023-03-28T00:00:00
[ [ "Ron", "Javier", "" ], [ "Soto-Valero", "César", "" ], [ "Zhang", "Long", "" ], [ "Baudry", "Benoit", "" ], [ "Monperrus", "Martin", "" ] ]
new_dataset
0.999188
2303.14441
Topside Mathonsi
Nombulelo Zulu, Deon P. Du Plessis, Topside E. Mathonsi and Tshimangadzo M. Tshilongamulenzhe
A User-Based Authentication and DoS Mitigation Scheme for Wearable Wireless Body Sensor Networks
null
null
null
null
cs.CR
http://creativecommons.org/publicdomain/zero/1.0/
Wireless Body Sensor Networks (WBSNs) is one of the greatest growing technology for sensing and performing various tasks. The information transmitted in the WBSNs is vulnerable to cyber-attacks, therefore security is very important. Denial of Service (DoS) attacks are considered one of the major threats against WBSNs security. In DoS attacks, an adversary targets to degrade and shut down the efficient use of the network and disrupt the services in the network causing them inaccessible to its intended users. If sensitive information of patients in WBSNs, such as the medical history is accessed by unauthorized users, the patient may suffer much more than the disease itself, it may result in loss of life. This paper proposes a User-Based authentication scheme to mitigate DoS attacks in WBSNs. A five-phase User-Based authentication DoS mitigation scheme for WBSNs is designed by integrating Elliptic Curve Cryptography (ECC) with Rivest Cipher 4 (RC4) to ensure a strong authentication process that will only allow authorized users to access nodes on WBSNs.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 11:32:16 GMT" } ]
2023-03-28T00:00:00
[ [ "Zulu", "Nombulelo", "" ], [ "Plessis", "Deon P. Du", "" ], [ "Mathonsi", "Topside E.", "" ], [ "Tshilongamulenzhe", "Tshimangadzo M.", "" ] ]
new_dataset
0.999058
2303.14474
Lei Wang
Lei Wang and Piotr Koniusz
3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition
This paper is accepted by CVPR 2023
CVPR 2023
null
null
cs.CV cs.AI cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Many skeletal action recognition models use GCNs to represent the human body by 3D body joints connected body parts. GCNs aggregate one- or few-hop graph neighbourhoods, and ignore the dependency between not linked body joints. We propose to form hypergraph to model hyper-edges between graph nodes (e.g., third- and fourth-order hyper-edges capture three and four nodes) which help capture higher-order motion patterns of groups of body joints. We split action sequences into temporal blocks, Higher-order Transformer (HoT) produces embeddings of each temporal block based on (i) the body joints, (ii) pairwise links of body joints and (iii) higher-order hyper-edges of skeleton body joints. We combine such HoT embeddings of hyper-edges of orders 1, ..., r by a novel Multi-order Multi-mode Transformer (3Mformer) with two modules whose order can be exchanged to achieve coupled-mode attention on coupled-mode tokens based on 'channel-temporal block', 'order-channel-body joint', 'channel-hyper-edge (any order)' and 'channel-only' pairs. The first module, called Multi-order Pooling (MP), additionally learns weighted aggregation along the hyper-edge mode, whereas the second module, Temporal block Pooling (TP), aggregates along the temporal block mode. Our end-to-end trainable network yields state-of-the-art results compared to GCN-, transformer- and hypergraph-based counterparts.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 14:06:31 GMT" } ]
2023-03-28T00:00:00
[ [ "Wang", "Lei", "" ], [ "Koniusz", "Piotr", "" ] ]
new_dataset
0.97882
2303.14482
Alborz Aghamaleki Sarvestani
Alborz Aghamaleki Sarvestani, Felix Ruppert, and Alexander Badri-Spr\"owitz
An Open-Source Modular Treadmill for Dynamic Force Measurement with Load Dependant Range Adjustment
null
null
null
null
cs.RO
http://creativecommons.org/licenses/by/4.0/
Ground reaction force sensing is one of the key components of gait analysis in legged locomotion research. To measure continuous force data during locomotion, we present a novel compound instrumented treadmill design. The treadmill is 1.7m long, with a natural frequency of 170Hz and an adjustable range that can be used for humans and small robots alike. Here, we present the treadmill's design methodology and characterize it in its natural frequency, noise behavior and real-life performance. Additionally, we apply an ISO 376 norm conform calibration procedure for all spatial force directions and center of pressure position. We achieve a force accuracy of $\leq$5.6N for the ground reaction forces and $\leq$13mm in center of pressure position.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 14:26:45 GMT" } ]
2023-03-28T00:00:00
[ [ "Sarvestani", "Alborz Aghamaleki", "" ], [ "Ruppert", "Felix", "" ], [ "Badri-Spröwitz", "Alexander", "" ] ]
new_dataset
0.998039
2303.14498
Wenqiang Xu
Wenqiang Xu, Zhenjun Yu, Han Xue, Ruolin Ye, Siqiong Yao, Cewu Lu
Visual-Tactile Sensing for In-Hand Object Reconstruction
Accepted in CVPR 2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Tactile sensing is one of the modalities humans rely on heavily to perceive the world. Working with vision, this modality refines local geometry structure, measures deformation at the contact area, and indicates the hand-object contact state. With the availability of open-source tactile sensors such as DIGIT, research on visual-tactile learning is becoming more accessible and reproducible. Leveraging this tactile sensor, we propose a novel visual-tactile in-hand object reconstruction framework \textbf{VTacO}, and extend it to \textbf{VTacOH} for hand-object reconstruction. Since our method can support both rigid and deformable object reconstruction, no existing benchmarks are proper for the goal. We propose a simulation environment, VT-Sim, which supports generating hand-object interaction for both rigid and deformable objects. With VT-Sim, we generate a large-scale training dataset and evaluate our method on it. Extensive experiments demonstrate that our proposed method can outperform the previous baseline methods qualitatively and quantitatively. Finally, we directly apply our model trained in simulation to various real-world test cases, which display qualitative results. Codes, models, simulation environment, and datasets are available at \url{https://sites.google.com/view/vtaco/}.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 15:16:31 GMT" } ]
2023-03-28T00:00:00
[ [ "Xu", "Wenqiang", "" ], [ "Yu", "Zhenjun", "" ], [ "Xue", "Han", "" ], [ "Ye", "Ruolin", "" ], [ "Yao", "Siqiong", "" ], [ "Lu", "Cewu", "" ] ]
new_dataset
0.996729
2303.14517
Surya Mahadi
Made Raharja Surya Mahadi and Nugraha Priya Utama
Indonesian Text-to-Image Synthesis with Sentence-BERT and FastGAN
11 pages, 3 figures
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Currently, text-to-image synthesis uses text encoder and image generator architecture. Research on this topic is challenging. This is because of the domain gap between natural language and vision. Nowadays, most research on this topic only focuses on producing a photo-realistic image, but the other domain, in this case, is the language, which is less concentrated. A lot of the current research uses English as the input text. Besides, there are many languages around the world. Bahasa Indonesia, as the official language of Indonesia, is quite popular. This language has been taught in Philipines, Australia, and Japan. Translating or recreating a new dataset into another language with good quality will cost a lot. Research on this domain is necessary because we need to examine how the image generator performs in other languages besides generating photo-realistic images. To achieve this, we translate the CUB dataset into Bahasa using google translate and manually by humans. We use Sentence BERT as the text encoder and FastGAN as the image generator. FastGAN uses lots of skip excitation modules and auto-encoder to generate an image with resolution 512x512x3, which is twice as bigger as the current state-of-the-art model (Zhang, Xu, Li, Zhang, Wang, Huang and Metaxas, 2019). We also get 4.76 +- 0.43 and 46.401 on Inception Score and Fr\'echet inception distance, respectively, and comparable with the current English text-to-image generation models. The mean opinion score also gives as 3.22 out of 5, which means the generated image is acceptable by humans. Link to source code: https://github.com/share424/Indonesian-Text-to-Image-synthesis-with-Sentence-BERT-and-FastGAN
[ { "version": "v1", "created": "Sat, 25 Mar 2023 16:54:22 GMT" } ]
2023-03-28T00:00:00
[ [ "Mahadi", "Made Raharja Surya", "" ], [ "Utama", "Nugraha Priya", "" ] ]
new_dataset
0.996046
2303.14521
M\'at\'e Cser\'ep
D\'avid Magyar, M\'at\'e Cser\'ep, Zolt\'an Vincell\'er, Attila D. Moln\'ar
Waste Detection and Change Analysis based on Multispectral Satellite Imagery
18 pages, 10 figures
In Proceedings of K\'EPAF 2023, Gyula, Hungary
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
One of the biggest environmental problems of our time is the increase in illegal landfills in forests, rivers, on river banks and other secluded places. In addition, waste in rivers causes damage not only locally, but also downstream, both in the water and washed ashore. Large islands of waste can also form at hydroelectric power stations and dams, and if they continue to flow, they can cause further damage to the natural environment along the river. Recent studies have also proved that rivers are the main source of plastic pollution in marine environments. Monitoring potential sources of danger is therefore highly important for effective waste collection for related organizations. In our research we analyze two possible forms of waste detection: identification of hot-spots (i.e. illegal waste dumps) and identification of water-surface river blockages. We used medium to high-resolution multispectral satellite imagery as our data source, especially focusing on the Tisza river as our study area. We found that using satellite imagery and machine learning are viable to locate and to monitor the change of the previously detected waste.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 17:12:22 GMT" } ]
2023-03-28T00:00:00
[ [ "Magyar", "Dávid", "" ], [ "Cserép", "Máté", "" ], [ "Vincellér", "Zoltán", "" ], [ "Molnár", "Attila D.", "" ] ]
new_dataset
0.998873
2303.14541
David Rozenberszki
David Rozenberszki, Or Litany, Angela Dai
UnScene3D: Unsupervised 3D Instance Segmentation for Indoor Scenes
Project page: https://rozdavid.github.io/unscene3d
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
3D instance segmentation is fundamental to geometric understanding of the world around us. Existing methods for instance segmentation of 3D scenes rely on supervision from expensive, manual 3D annotations. We propose UnScene3D, the first fully unsupervised 3D learning approach for class-agnostic 3D instance segmentation of indoor scans. UnScene3D first generates pseudo masks by leveraging self-supervised color and geometry features to find potential object regions. We operate on a basis of geometric oversegmentation, enabling efficient representation and learning on high-resolution 3D data. The coarse proposals are then refined through self-training our model on its predictions. Our approach improves over state-of-the-art unsupervised 3D instance segmentation methods by more than 300% Average Precision score, demonstrating effective instance segmentation even in challenging, cluttered 3D scenes.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 19:15:16 GMT" } ]
2023-03-28T00:00:00
[ [ "Rozenberszki", "David", "" ], [ "Litany", "Or", "" ], [ "Dai", "Angela", "" ] ]
new_dataset
0.9987
2303.14557
Zhuoyue Lyu
Zhuoyue Lyu
Clo(o)k: A Clock That Looks
CHI '23 Human Computer Interaction Across Borders (HCIxB) Workshop Papers
null
null
null
cs.HC
http://creativecommons.org/licenses/by-nc-sa/4.0/
What if a clock could do more than just tell time - what if it could actually see? This paper delves into the conceptualization, design, and construction of a timepiece with visual perception capabilities, featuring three applications that expand the possibilities of human-time interaction. Insights from an Open House showcase are also shared, highlighting the unique user experiences of this device.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 20:49:40 GMT" } ]
2023-03-28T00:00:00
[ [ "Lyu", "Zhuoyue", "" ] ]
new_dataset
0.999275
2303.14587
Shuhong Chen
Shuhong Chen, Kevin Zhang, Yichun Shi, Heng Wang, Yiheng Zhu, Guoxian Song, Sizhe An, Janus Kristjansson, Xiao Yang, Matthias Zwicker
PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters
CVPR 2023, code release: https://github.com/ShuhongChen/panic3d-anime-reconstruction
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
We propose PAniC-3D, a system to reconstruct stylized 3D character heads directly from illustrated (p)ortraits of (ani)me (c)haracters. Our anime-style domain poses unique challenges to single-view reconstruction; compared to natural images of human heads, character portrait illustrations have hair and accessories with more complex and diverse geometry, and are shaded with non-photorealistic contour lines. In addition, there is a lack of both 3D model and portrait illustration data suitable to train and evaluate this ambiguous stylized reconstruction task. Facing these challenges, our proposed PAniC-3D architecture crosses the illustration-to-3D domain gap with a line-filling model, and represents sophisticated geometries with a volumetric radiance field. We train our system with two large new datasets (11.2k Vroid 3D models, 1k Vtuber portrait illustrations), and evaluate on a novel AnimeRecon benchmark of illustration-to-3D pairs. PAniC-3D significantly outperforms baseline methods, and provides data to establish the task of stylized reconstruction from portrait illustrations.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 23:36:17 GMT" } ]
2023-03-28T00:00:00
[ [ "Chen", "Shuhong", "" ], [ "Zhang", "Kevin", "" ], [ "Shi", "Yichun", "" ], [ "Wang", "Heng", "" ], [ "Zhu", "Yiheng", "" ], [ "Song", "Guoxian", "" ], [ "An", "Sizhe", "" ], [ "Kristjansson", "Janus", "" ], [ "Yang", "Xiao", "" ], [ "Zwicker", "Matthias", "" ] ]
new_dataset
0.997763
2303.14588
Bashar Al-Rfooh
Bashar Al-Rfooh, Gheith Abandah, Rami Al-Rfou
Fine-Tashkeel: Finetuning Byte-Level Models for Accurate Arabic Text Diacritization
null
null
null
null
cs.CL
http://creativecommons.org/licenses/by/4.0/
Most of previous work on learning diacritization of the Arabic language relied on training models from scratch. In this paper, we investigate how to leverage pre-trained language models to learn diacritization. We finetune token-free pre-trained multilingual models (ByT5) to learn to predict and insert missing diacritics in Arabic text, a complex task that requires understanding the sentence semantics and the morphological structure of the tokens. We show that we can achieve state-of-the-art on the diacritization task with minimal amount of training and no feature engineering, reducing WER by 40%. We release our finetuned models for the greater benefit of the researchers in the community.
[ { "version": "v1", "created": "Sat, 25 Mar 2023 23:41:33 GMT" } ]
2023-03-28T00:00:00
[ [ "Al-Rfooh", "Bashar", "" ], [ "Abandah", "Gheith", "" ], [ "Al-Rfou", "Rami", "" ] ]
new_dataset
0.976141
2303.14597
Sangchul Park
Sangchul Park
Smart Cities: Striking a Balance Between Urban Resilience and Civil Liberties
null
null
null
null
cs.CY
http://creativecommons.org/licenses/by/4.0/
Cities are becoming smarter and more resilient by integrating urban infrastructure with information technology. However, concerns grow that smart cities might reverse progress on civil liberties when sensing, profiling, and predicting citizen activities; undermining citizen autonomy in connectivity, mobility, and energy consumption; and deprivatizing digital infrastructure. In response, cities need to deploy technical breakthroughs, such as privacy-enhancing technologies, cohort modelling, and fair and explainable machine learning. However, as throwing technologies at cities cannot always address civil liberty concerns, cities must ensure transparency and foster citizen participation to win public trust about the way resilience and liberties are balanced.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 01:09:11 GMT" } ]
2023-03-28T00:00:00
[ [ "Park", "Sangchul", "" ] ]
new_dataset
0.994076
2303.14614
Kai Niu
Kai Niu, Ping Zhang, Jincheng Dai, Zhongwei Si, Chao Dong
A Golden Decade of Polar Codes: From Basic Principle to 5G Applications
29 pages, 21 figures, Published in China Communications
China Communications, vol.20, no. 2, pp. 94-121, 2023
10.23919/JCC.2023.02.015
null
cs.IT math.IT
http://creativecommons.org/licenses/by/4.0/
After the pursuit of seventy years, the invention of polar codes indicates that we have found the first capacity-achieving coding with low complexity construction and decoding, which is the great breakthrough of the coding theory in the past two decades. In this survey, we retrospect the history of polar codes and summarize the advancement in the past ten years. First, the primary principle of channel polarization is investigated such that the basic construction, coding method, and classic successive cancellation (SC) decoding are reviewed. Second, in order to improve the performance of the finite code length, we introduce the guiding principle and conclude five design criteria for the construction, design, and implementation of the polar code in the practical communication system based on the exemplar schemes in the literature. Especially, we explain the design principle behind the concatenated coding and rate matching of polar codes in a 5G wireless system. Furthermore, the improved SC decoding algorithms, such as SC list (SCL) decoding and SC stack (SCS) decoding, etc., are investigated and compared. Finally, the research prospects of polar codes for the future 6G communication system are explored, including the optimization of short polar codes, coding construction in fading channels, polar coded modulation and HARQ, and the polar coded transmission, namely polar processing. Predictably, as a new coding methodology, polar codes will shine a light on communication theory and unveil a revolution in transmission technology.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 03:42:59 GMT" } ]
2023-03-28T00:00:00
[ [ "Niu", "Kai", "" ], [ "Zhang", "Ping", "" ], [ "Dai", "Jincheng", "" ], [ "Si", "Zhongwei", "" ], [ "Dong", "Chao", "" ] ]
new_dataset
0.981943
2303.14626
Yukang Zhang
Yukang Zhang, Yan Yan, Jie Li, Hanzi Wang
MRCN: A Novel Modality Restitution and Compensation Network for Visible-Infrared Person Re-identification
Accepted by AAAI-2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Visible-infrared person re-identification (VI-ReID), which aims to search identities across different spectra, is a challenging task due to large cross-modality discrepancy between visible and infrared images. The key to reduce the discrepancy is to filter out identity-irrelevant interference and effectively learn modality-invariant person representations. In this paper, we propose a novel Modality Restitution and Compensation Network (MRCN) to narrow the gap between the two modalities. Specifically, we first reduce the modality discrepancy by using two Instance Normalization (IN) layers. Next, to reduce the influence of IN layers on removing discriminative information and to reduce modality differences, we propose a Modality Restitution Module (MRM) and a Modality Compensation Module (MCM) to respectively distill modality-irrelevant and modality-relevant features from the removed information. Then, the modality-irrelevant features are used to restitute to the normalized visible and infrared features, while the modality-relevant features are used to compensate for the features of the other modality. Furthermore, to better disentangle the modality-relevant features and the modality-irrelevant features, we propose a novel Center-Quadruplet Causal (CQC) loss to encourage the network to effectively learn the modality-relevant features and the modality-irrelevant features. Extensive experiments are conducted to validate the superiority of our method on the challenging SYSU-MM01 and RegDB datasets. More remarkably, our method achieves 95.1% in terms of Rank-1 and 89.2% in terms of mAP on the RegDB dataset.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 05:03:18 GMT" } ]
2023-03-28T00:00:00
[ [ "Zhang", "Yukang", "" ], [ "Yan", "Yan", "" ], [ "Li", "Jie", "" ], [ "Wang", "Hanzi", "" ] ]
new_dataset
0.998608
2303.14647
Behrouz Minaei-Bidgoli
Najmeh Torabian, Behrouz Minaei-Bidgoli and Mohsen Jahanshahi
Farspredict: A benchmark dataset for link prediction
13 pages, 3 figures, 1 algorithm and 5 tables
null
null
null
cs.AI cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Link prediction with knowledge graph embedding (KGE) is a popular method for knowledge graph completion. Furthermore, training KGEs on non-English knowledge graph promote knowledge extraction and knowledge graph reasoning in the context of these languages. However, many challenges in non-English KGEs pose to learning a low-dimensional representation of a knowledge graph's entities and relations. This paper proposes "Farspredict" a Persian knowledge graph based on Farsbase (the most comprehensive knowledge graph in Persian). It also explains how the knowledge graph structure affects link prediction accuracy in KGE. To evaluate Farspredict, we implemented the popular models of KGE on it and compared the results with Freebase. Given the analysis results, some optimizations on the knowledge graph are carried out to improve its functionality in the KGE. As a result, a new Persian knowledge graph is achieved. Implementation results in the KGE models on Farspredict outperforming Freebases in many cases. At last, we discuss what improvements could be effective in enhancing the quality of Farspredict and how much it improves.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 07:41:26 GMT" } ]
2023-03-28T00:00:00
[ [ "Torabian", "Najmeh", "" ], [ "Minaei-Bidgoli", "Behrouz", "" ], [ "Jahanshahi", "Mohsen", "" ] ]
new_dataset
0.999647
2303.14653
Min Wang
Yingda Guan, Zhengyang Feng, Huiying Chang, Kuo Du, Tingting Li, Min Wang
SDTracker: Synthetic Data Based Multi-Object Tracking
cvpr2022 workshop
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present SDTracker, a method that harnesses the potential of synthetic data for multi-object tracking of real-world scenes in a domain generalization and semi-supervised fashion. First, we use the ImageNet dataset as an auxiliary to randomize the style of synthetic data. With out-of-domain data, we further enforce pyramid consistency loss across different "stylized" images from the same sample to learn domain invariant features. Second, we adopt the pseudo-labeling method to effectively utilize the unlabeled MOT17 training data. To obtain high-quality pseudo-labels, we apply proximal policy optimization (PPO2) algorithm to search confidence thresholds for each sequence. When using the unlabeled MOT17 training set, combined with the pure-motion tracking strategy upgraded via developed post-processing, we finally reach 61.4 HOTA.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 08:21:22 GMT" } ]
2023-03-28T00:00:00
[ [ "Guan", "Yingda", "" ], [ "Feng", "Zhengyang", "" ], [ "Chang", "Huiying", "" ], [ "Du", "Kuo", "" ], [ "Li", "Tingting", "" ], [ "Wang", "Min", "" ] ]
new_dataset
0.992065
2303.14706
Qian Wang
Qian Wang, Yiqun Wang, Michael Birsak, Peter Wonka
BlobGAN-3D: A Spatially-Disentangled 3D-Aware Generative Model for Indoor Scenes
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
3D-aware image synthesis has attracted increasing interest as it models the 3D nature of our real world. However, performing realistic object-level editing of the generated images in the multi-object scenario still remains a challenge. Recently, a 2D GAN termed BlobGAN has demonstrated great multi-object editing capabilities on real-world indoor scene datasets. In this work, we propose BlobGAN-3D, which is a 3D-aware improvement of the original 2D BlobGAN. We enable explicit camera pose control while maintaining the disentanglement for individual objects in the scene by extending the 2D blobs into 3D blobs. We keep the object-level editing capabilities of BlobGAN and in addition allow flexible control over the 3D location of the objects in the scene. We test our method on real-world indoor datasets and show that our method can achieve comparable image quality compared to the 2D BlobGAN and other 3D-aware GAN baselines while being able to enable camera pose control and object-level editing in the challenging multi-object real-world scenarios.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 12:23:11 GMT" } ]
2023-03-28T00:00:00
[ [ "Wang", "Qian", "" ], [ "Wang", "Yiqun", "" ], [ "Birsak", "Michael", "" ], [ "Wonka", "Peter", "" ] ]
new_dataset
0.999802
2303.14707
Xinhang Liu
Xinhang Liu, Yu-Wing Tai, Chi-Keung Tang
Clean-NeRF: Reformulating NeRF to account for View-Dependent Observations
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by-nc-nd/4.0/
While Neural Radiance Fields (NeRFs) had achieved unprecedented novel view synthesis results, they have been struggling in dealing with large-scale cluttered scenes with sparse input views and highly view-dependent appearances. Specifically, existing NeRF-based models tend to produce blurry rendering with the volumetric reconstruction often inaccurate, where a lot of reconstruction errors are observed in the form of foggy "floaters" hovering within the entire volume of an opaque 3D scene. Such inaccuracies impede NeRF's potential for accurate 3D NeRF registration, object detection, segmentation, etc., which possibly accounts for only limited significant research effort so far to directly address these important 3D fundamental computer vision problems to date. This paper analyzes the NeRF's struggles in such settings and proposes Clean-NeRF for accurate 3D reconstruction and novel view rendering in complex scenes. Our key insights consist of enforcing effective appearance and geometry constraints, which are absent in the conventional NeRF reconstruction, by 1) automatically detecting and modeling view-dependent appearances in the training views to prevent them from interfering with density estimation, which is complete with 2) a geometric correction procedure performed on each traced ray during inference. Clean-NeRF can be implemented as a plug-in that can immediately benefit existing NeRF-based methods without additional input. Codes will be released.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 12:24:31 GMT" } ]
2023-03-28T00:00:00
[ [ "Liu", "Xinhang", "" ], [ "Tai", "Yu-Wing", "" ], [ "Tang", "Chi-Keung", "" ] ]
new_dataset
0.983765
2303.14717
Jianhui Yu
Jianhui Yu, Hao Zhu, Liming Jiang, Chen Change Loy, Weidong Cai, Wayne Wu
CelebV-Text: A Large-Scale Facial Text-Video Dataset
Accepted by CVPR2023. Project Page: https://celebv-text.github.io/
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Text-driven generation models are flourishing in video generation and editing. However, face-centric text-to-video generation remains a challenge due to the lack of a suitable dataset containing high-quality videos and highly relevant texts. This paper presents CelebV-Text, a large-scale, diverse, and high-quality dataset of facial text-video pairs, to facilitate research on facial text-to-video generation tasks. CelebV-Text comprises 70,000 in-the-wild face video clips with diverse visual content, each paired with 20 texts generated using the proposed semi-automatic text generation strategy. The provided texts are of high quality, describing both static and dynamic attributes precisely. The superiority of CelebV-Text over other datasets is demonstrated via comprehensive statistical analysis of the videos, texts, and text-video relevance. The effectiveness and potential of CelebV-Text are further shown through extensive self-evaluation. A benchmark is constructed with representative methods to standardize the evaluation of the facial text-to-video generation task. All data and models are publicly available.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 13:06:35 GMT" } ]
2023-03-28T00:00:00
[ [ "Yu", "Jianhui", "" ], [ "Zhu", "Hao", "" ], [ "Jiang", "Liming", "" ], [ "Loy", "Chen Change", "" ], [ "Cai", "Weidong", "" ], [ "Wu", "Wayne", "" ] ]
new_dataset
0.999909
2303.14718
Kazuki Sugihara
Kazuki Sugihara, Moju Zhao, Takuzumi Nishio, Tasuku Makabe, Kei Okada, Masayuki Inaba
Design and Control of a Humanoid Equipped with Flight Unit and Wheels for Multimodal Locomotion
8 pages 17 figures
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Humanoids are versatile robotic platforms because of their limbs with multiple degrees of freedom. Although humanoids can walk like humans, the speed is relatively slow, and they cannot run over large barriers. To address these problems, we aim to achieve rapid terrestrial locomotion ability and simultaneously expand the domain of locomotion to the air by utilizing thrust for propulsion. In this paper, we first describe an optimized construction method of a humanoid robot equipped with wheels and a flight unit to achieve these abilities. Then, we describe the integrated control framework of the proposed flying humanoid for each mode of locomotion: aerial locomotion, leg locomotion, and wheel locomotion. Finally, we achieved multimodal locomotion and aerial manipulation experiments using the robot platform proposed in this work. To the best of our knowledge, it is the first time to achieve three different types of locomotion, including flight, by a single humanoid.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 13:09:14 GMT" } ]
2023-03-28T00:00:00
[ [ "Sugihara", "Kazuki", "" ], [ "Zhao", "Moju", "" ], [ "Nishio", "Takuzumi", "" ], [ "Makabe", "Tasuku", "" ], [ "Okada", "Kei", "" ], [ "Inaba", "Masayuki", "" ] ]
new_dataset
0.990742
2303.14758
Asma Jodeiri Akbarfam
Asma Jodeiri Akbarfam, Sina Barazandeh, Hoda Maleki, Deepti Gupta
DLACB: Deep Learning Based Access Control Using Blockchain
null
null
null
null
cs.CR
http://creativecommons.org/licenses/by-nc-nd/4.0/
In general, deep learning models use to make informed decisions immensely. Developed models are mainly based on centralized servers, which face several issues, including transparency, traceability, reliability, security, and privacy. In this research, we identify a research gap in a distributed nature-based access control that can solve those issues. The innovative technology blockchain could fill this gap and provide a robust solution. Blockchain's immutable and distributed nature designs a useful framework in various domains such as medicine, finance, and government, which can also provide access control as opposed to centralized methods that rely on trusted third parties to access the resources. In existing frameworks, a traditional access control approach is developed using blockchain, which depends on predefined policies and permissions that are not reliable. In this research, we propose DLACB: Deep Learning Based Access Control Using Blockchain, which utilizes a deep learning access control mechanism to determine a user's permissions on a given resource. This proposed framework authenticates the users and logs the access requests on the blockchain to recognize malicious users. The results show that this proposed framework operates correctly for all possible scenarios.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 15:25:09 GMT" } ]
2023-03-28T00:00:00
[ [ "Akbarfam", "Asma Jodeiri", "" ], [ "Barazandeh", "Sina", "" ], [ "Maleki", "Hoda", "" ], [ "Gupta", "Deepti", "" ] ]
new_dataset
0.998522
2303.14792
Mehdi Delrobaei
Fateme Zare, Paniz Sedighi, Mehdi Delrobaei
A Wearable RFID-Based Navigation System for the Visually Impaired
6 pages, 6 figures, 3 tables
null
10.1109/ICRoM57054.2022.10025351
null
cs.HC cs.SY eess.SY
http://creativecommons.org/licenses/by-nc-nd/4.0/
Recent studies have focused on developing advanced assistive devices to help blind or visually impaired people. Navigation is challenging for this community; however, developing a simple yet reliable navigation system is still an unmet need. This study targets the navigation problem and proposes a wearable assistive system. We developed a smart glove and shoe set based on radio-frequency identification technology to assist visually impaired people with navigation and orientation in indoor environments. The system enables the user to find the directions through audio feedback. To evaluate the device's performance, we designed a simple experimental setup. The proposed system has a simple structure and can be personalized according to the user's requirements. The results identified that the platform is reliable, power efficient, and accurate enough for indoor navigation.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 18:30:57 GMT" } ]
2023-03-28T00:00:00
[ [ "Zare", "Fateme", "" ], [ "Sedighi", "Paniz", "" ], [ "Delrobaei", "Mehdi", "" ] ]
new_dataset
0.996849
2303.14796
Hadar Frenkel
Bernd Finkbeiner, Hadar Frenkel, Jana Hofmann, and Janine Lohse
Automata-Based Software Model Checking of Hyperproperties
null
null
null
null
cs.LO cs.FL
http://creativecommons.org/licenses/by/4.0/
We develop model checking algorithms for Temporal Stream Logic (TSL) and Hyper Temporal Stream Logic (HyperTSL) modulo theories. TSL extends Linear Temporal Logic (LTL) with memory cells, functions and predicates, making it a convenient and expressive logic to reason over software and other systems with infinite data domains. HyperTSL further extends TSL to the specification of hyperproperties - properties that relate multiple system executions. As such, HyperTSL can express information flow policies like noninterference in software systems. We augment HyperTSL with theories, resulting in HyperTSL(T),and build on methods from LTL software verification to obtain model checking algorithms for TSL and HyperTSL(T). This results in a sound but necessarily incomplete algorithm for specifications contained in the forall*exists* fragment of HyperTSL(T). Our approach constitutes the first software model checking algorithm for temporal hyperproperties with quantifier alternations that does not rely on a finite-state abstraction.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 19:01:10 GMT" } ]
2023-03-28T00:00:00
[ [ "Finkbeiner", "Bernd", "" ], [ "Frenkel", "Hadar", "" ], [ "Hofmann", "Jana", "" ], [ "Lohse", "Janine", "" ] ]
new_dataset
0.971936
2303.14814
Yang Zou
Jongheon Jeong, Yang Zou, Taewan Kim, Dongqing Zhang, Avinash Ravichandran, Onkar Dabeer
WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
Accepted to Conference on Computer Vision and Pattern Recognition (CVPR) 2023
null
null
null
cs.CV cs.AI cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Visual anomaly classification and segmentation are vital for automating industrial quality inspection. The focus of prior research in the field has been on training custom models for each quality inspection task, which requires task-specific images and annotation. In this paper we move away from this regime, addressing zero-shot and few-normal-shot anomaly classification and segmentation. Recently CLIP, a vision-language model, has shown revolutionary generality with competitive zero-/few-shot performance in comparison to full-supervision. But CLIP falls short on anomaly classification and segmentation tasks. Hence, we propose window-based CLIP (WinCLIP) with (1) a compositional ensemble on state words and prompt templates and (2) efficient extraction and aggregation of window/patch/image-level features aligned with text. We also propose its few-normal-shot extension WinCLIP+, which uses complementary information from normal images. In MVTec-AD (and VisA), without further tuning, WinCLIP achieves 91.8%/85.1% (78.1%/79.6%) AUROC in zero-shot anomaly classification and segmentation while WinCLIP+ does 93.1%/95.2% (83.8%/96.4%) in 1-normal-shot, surpassing state-of-the-art by large margins.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 20:41:21 GMT" } ]
2023-03-28T00:00:00
[ [ "Jeong", "Jongheon", "" ], [ "Zou", "Yang", "" ], [ "Kim", "Taewan", "" ], [ "Zhang", "Dongqing", "" ], [ "Ravichandran", "Avinash", "" ], [ "Dabeer", "Onkar", "" ] ]
new_dataset
0.994434
2303.14816
Tian-Zhu Xiang
Zhou Huang, Hang Dai, Tian-Zhu Xiang, Shuo Wang, Huai-Xin Chen, Jie Qin, Huan Xiong
Feature Shrinkage Pyramid for Camouflaged Object Detection with Transformers
CVPR 2023. Project webpage at: https://tzxiang.github.io/project/COD-FSPNet/index.html
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Vision transformers have recently shown strong global context modeling capabilities in camouflaged object detection. However, they suffer from two major limitations: less effective locality modeling and insufficient feature aggregation in decoders, which are not conducive to camouflaged object detection that explores subtle cues from indistinguishable backgrounds. To address these issues, in this paper, we propose a novel transformer-based Feature Shrinkage Pyramid Network (FSPNet), which aims to hierarchically decode locality-enhanced neighboring transformer features through progressive shrinking for camouflaged object detection. Specifically, we propose a nonlocal token enhancement module (NL-TEM) that employs the non-local mechanism to interact neighboring tokens and explore graph-based high-order relations within tokens to enhance local representations of transformers. Moreover, we design a feature shrinkage decoder (FSD) with adjacent interaction modules (AIM), which progressively aggregates adjacent transformer features through a layer-bylayer shrinkage pyramid to accumulate imperceptible but effective cues as much as possible for object information decoding. Extensive quantitative and qualitative experiments demonstrate that the proposed model significantly outperforms the existing 24 competitors on three challenging COD benchmark datasets under six widely-used evaluation metrics. Our code is publicly available at https://github.com/ZhouHuang23/FSPNet.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 20:50:58 GMT" } ]
2023-03-28T00:00:00
[ [ "Huang", "Zhou", "" ], [ "Dai", "Hang", "" ], [ "Xiang", "Tian-Zhu", "" ], [ "Wang", "Shuo", "" ], [ "Chen", "Huai-Xin", "" ], [ "Qin", "Jie", "" ], [ "Xiong", "Huan", "" ] ]
new_dataset
0.982287
2303.14827
Benjamin Kenwright
Ben Kenwright
Dual-Quaternion Julia Fractals
null
null
null
null
cs.CG
http://creativecommons.org/licenses/by/4.0/
Fractals offer the ability to generate fascinating geometric shapes with all sorts of unique characteristics (for instance, fractal geometry provides a basis for modelling infinite detail found in nature). While fractals are non-euclidean mathematical objects which possess an assortment of properties (e.g., attractivity and symmetry), they are also able to be scaled down, rotated, skewed and replicated in embedded contexts. Hence, many different types of fractals have come into limelight since their origin discovery. One particularly popular method for generating fractal geometry is using Julia sets. Julia sets provide a straightforward and innovative method for generating fractal geometry using an iterative computational modelling algorithm. In this paper, we present a method that combines Julia sets with dual-quaternion algebra. Dual-quaternions are an alluring principal with a whole range interesting mathematical possibilities. Extending fractal Julia sets to encompass dual-quaternions algebra provides us with a novel visualize solution. We explain the method of fractals using the dual-quaternions in combination with Julia sets. Our prototype implementation demonstrate an efficient methods for rendering fractal geometry using dual-quaternion Julia sets based upon an uncomplicated ray tracing algorithm. We show a number of different experimental isosurface examples to demonstrate the viability of our approach.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 21:20:31 GMT" } ]
2023-03-28T00:00:00
[ [ "Kenwright", "Ben", "" ] ]
new_dataset
0.998828