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2303.14848
Zuhaib Akhtar
Zuhaib Akhtar
From Blockchain to Hashgraph: Distributed Ledger Technologies in the Wild
null
null
null
null
cs.DC
http://creativecommons.org/licenses/by/4.0/
With the introduction of the term blockchain in 2008, its interest has been increasing in the community since the idea was coined. The reason for this interest is because it provides anonymity, security and integrity without any central third party organisation in control of data and transaction. It has attracted huge interest in research areas due to its advances in various platforms, limitations and challenges. There are various Distributed Ledger Technologies that demonstrates their special features which overcome limitations of other platforms. However, implementations of various distributed ledger technologies differ substantially based on their data structures, consensus protocol and fault tolerant among others. Due to these variations, they have a quite different cost, performance, latency and security. In this paper, working and in-depth comparison of major distributed ledger technologies including their special features, strengths and weaknesses is presented and discussed by identifying various criteria.
[ { "version": "v1", "created": "Sun, 26 Mar 2023 23:26:46 GMT" } ]
2023-03-28T00:00:00
[ [ "Akhtar", "Zuhaib", "" ] ]
new_dataset
0.992487
2303.14883
Sandra Liu
Sandra Q. Liu, Yuxiang Ma, Edward H. Adelson
GelSight Baby Fin Ray: A Compact, Compliant, Flexible Finger with High-Resolution Tactile Sensing
Accepted to IEEE Conference of Soft Robotics (RoboSoft) 2023
null
null
null
cs.RO
http://creativecommons.org/licenses/by/4.0/
The synthesis of tactile sensing with compliance is essential to many fields, from agricultural usages like fruit picking, to sustainability practices such as sorting recycling, to the creation of safe home-care robots for the elderly to age with dignity. From tactile sensing, we can discern material properties, recognize textures, and determine softness, while with compliance, we are able to securely and safely interact with the objects and the environment around us. These two abilities can culminate into a useful soft robotic gripper, such as the original GelSight Fin Ray, which is able to grasp a large variety of different objects and also perform a simple household manipulation task: wine glass reorientation. Although the original GelSight Fin Ray solves the problem of interfacing a generally rigid, high-resolution sensor with a soft, compliant structure, we can improve the robustness of the sensor and implement techniques that make such camera-based tactile sensors applicable to a wider variety of soft robot designs. We first integrate flexible mirrors and incorporate the rigid electronic components into the base of the gripper, which greatly improves the compliance of the Fin Ray structure. Then, we synthesize a flexible and high-elongation silicone adhesive-based fluorescent paint, which can provide good quality 2D tactile localization results for our sensor. Finally, we incorporate all of these techniques into a new design: the Baby Fin Ray, which we use to dig through clutter, and perform successful classification of nuts in their shells. The supplementary video can be found here: https://youtu.be/_oD_QFtYTPM
[ { "version": "v1", "created": "Mon, 27 Mar 2023 02:47:19 GMT" } ]
2023-03-28T00:00:00
[ [ "Liu", "Sandra Q.", "" ], [ "Ma", "Yuxiang", "" ], [ "Adelson", "Edward H.", "" ] ]
new_dataset
0.998849
2303.14884
Shuangping Huang
Fan Yang, Lei Hu, Xinwu Liu, Shuangping Huang, Zhenghui Gu
A large-scale dataset for end-to-end table recognition in the wild
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Table recognition (TR) is one of the research hotspots in pattern recognition, which aims to extract information from tables in an image. Common table recognition tasks include table detection (TD), table structure recognition (TSR) and table content recognition (TCR). TD is to locate tables in the image, TCR recognizes text content, and TSR recognizes spatial ogical structure. Currently, the end-to-end TR in real scenarios, accomplishing the three sub-tasks simultaneously, is yet an unexplored research area. One major factor that inhibits researchers is the lack of a benchmark dataset. To this end, we propose a new large-scale dataset named Table Recognition Set (TabRecSet) with diverse table forms sourcing from multiple scenarios in the wild, providing complete annotation dedicated to end-to-end TR research. It is the largest and first bi-lingual dataset for end-to-end TR, with 38.1K tables in which 20.4K are in English\, and 17.7K are in Chinese. The samples have diverse forms, such as the border-complete and -incomplete table, regular and irregular table (rotated, distorted, etc.). The scenarios are multiple in the wild, varying from scanned to camera-taken images, documents to Excel tables, educational test papers to financial invoices. The annotations are complete, consisting of the table body spatial annotation, cell spatial logical annotation and text content for TD, TSR and TCR, respectively. The spatial annotation utilizes the polygon instead of the bounding box or quadrilateral adopted by most datasets. The polygon spatial annotation is more suitable for irregular tables that are common in wild scenarios. Additionally, we propose a visualized and interactive annotation tool named TableMe to improve the efficiency and quality of table annotation.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 02:48:51 GMT" } ]
2023-03-28T00:00:00
[ [ "Yang", "Fan", "" ], [ "Hu", "Lei", "" ], [ "Liu", "Xinwu", "" ], [ "Huang", "Shuangping", "" ], [ "Gu", "Zhenghui", "" ] ]
new_dataset
0.999899
2303.14935
Nam Ly Tuan
Phuc Nguyen, Nam Tuan Ly, Hideaki Takeda, and Atsuhiro Takasu
TabIQA: Table Questions Answering on Business Document Images
First two authors contributed equally
null
null
null
cs.CV cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Table answering questions from business documents has many challenges that require understanding tabular structures, cross-document referencing, and additional numeric computations beyond simple search queries. This paper introduces a novel pipeline, named TabIQA, to answer questions about business document images. TabIQA combines state-of-the-art deep learning techniques 1) to extract table content and structural information from images and 2) to answer various questions related to numerical data, text-based information, and complex queries from structured tables. The evaluation results on VQAonBD 2023 dataset demonstrate the effectiveness of TabIQA in achieving promising performance in answering table-related questions. The TabIQA repository is available at https://github.com/phucty/itabqa.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 06:31:21 GMT" } ]
2023-03-28T00:00:00
[ [ "Nguyen", "Phuc", "" ], [ "Ly", "Nam Tuan", "" ], [ "Takeda", "Hideaki", "" ], [ "Takasu", "Atsuhiro", "" ] ]
new_dataset
0.990408
2303.15060
Dongki Jung
Jaehoon Choi, Dongki Jung, Taejae Lee, Sangwook Kim, Youngdong Jung, Dinesh Manocha, Donghwan Lee
TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering
Accepted to CVPR23. Project Page: https://jh-choi.github.io/TMO/
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses. Our method first introduces an RGBD-aided structure from motion, which can yield filtered depth maps and refines camera poses guided by corresponding depth. Then, we adopt the neural implicit surface reconstruction method, which allows for high-quality mesh and develops a new training process for applying a regularization provided by classical multi-view stereo methods. Moreover, we apply a differentiable rendering to fine-tune incomplete texture maps and generate textures which are perceptually closer to the original scene. Our pipeline can be applied to any common objects in the real world without the need for either in-the-lab environments or accurate mask images. We demonstrate results of captured objects with complex shapes and validate our method numerically against existing 3D reconstruction and texture mapping methods.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 10:07:52 GMT" } ]
2023-03-28T00:00:00
[ [ "Choi", "Jaehoon", "" ], [ "Jung", "Dongki", "" ], [ "Lee", "Taejae", "" ], [ "Kim", "Sangwook", "" ], [ "Jung", "Youngdong", "" ], [ "Manocha", "Dinesh", "" ], [ "Lee", "Donghwan", "" ] ]
new_dataset
0.998596
2303.15083
Shengchao Zhou
Shengchao Zhou, Weizhou Liu, Chen Hu, Shuchang Zhou, and Chao Ma
UniDistill: A Universal Cross-Modality Knowledge Distillation Framework for 3D Object Detection in Bird's-Eye View
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In the field of 3D object detection for autonomous driving, the sensor portfolio including multi-modality and single-modality is diverse and complex. Since the multi-modal methods have system complexity while the accuracy of single-modal ones is relatively low, how to make a tradeoff between them is difficult. In this work, we propose a universal cross-modality knowledge distillation framework (UniDistill) to improve the performance of single-modality detectors. Specifically, during training, UniDistill projects the features of both the teacher and the student detector into Bird's-Eye-View (BEV), which is a friendly representation for different modalities. Then, three distillation losses are calculated to sparsely align the foreground features, helping the student learn from the teacher without introducing additional cost during inference. Taking advantage of the similar detection paradigm of different detectors in BEV, UniDistill easily supports LiDAR-to-camera, camera-to-LiDAR, fusion-to-LiDAR and fusion-to-camera distillation paths. Furthermore, the three distillation losses can filter the effect of misaligned background information and balance between objects of different sizes, improving the distillation effectiveness. Extensive experiments on nuScenes demonstrate that UniDistill effectively improves the mAP and NDS of student detectors by 2.0%~3.2%.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 10:50:58 GMT" } ]
2023-03-28T00:00:00
[ [ "Zhou", "Shengchao", "" ], [ "Liu", "Weizhou", "" ], [ "Hu", "Chen", "" ], [ "Zhou", "Shuchang", "" ], [ "Ma", "Chao", "" ] ]
new_dataset
0.972095
2303.15110
Isaac Chung
Elizaveta Korotkova, Isaac Kwan Yin Chung
Beyond Toxic: Toxicity Detection Datasets are Not Enough for Brand Safety
null
null
null
null
cs.CL cs.AI
http://creativecommons.org/licenses/by-sa/4.0/
The rapid growth in user generated content on social media has resulted in a significant rise in demand for automated content moderation. Various methods and frameworks have been proposed for the tasks of hate speech detection and toxic comment classification. In this work, we combine common datasets to extend these tasks to brand safety. Brand safety aims to protect commercial branding by identifying contexts where advertisements should not appear and covers not only toxicity, but also other potentially harmful content. As these datasets contain different label sets, we approach the overall problem as a binary classification task. We demonstrate the need for building brand safety specific datasets via the application of common toxicity detection datasets to a subset of brand safety and empirically analyze the effects of weighted sampling strategies in text classification.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 11:29:09 GMT" } ]
2023-03-28T00:00:00
[ [ "Korotkova", "Elizaveta", "" ], [ "Chung", "Isaac Kwan Yin", "" ] ]
new_dataset
0.997912
2303.15128
Timo H\"ackel
Mehmet Mueller, Timo H\"ackel, Philipp Meyer, Franz Korf, Thomas C. Schmidt
Authenticated and Secure Automotive Service Discovery with DNSSEC and DANE
null
null
null
null
cs.CR cs.NI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Automotive softwarization is progressing and future cars are expected to operate a Service-Oriented Architecture on multipurpose compute units, which are interconnected via a high-speed Ethernet backbone. The AUTOSAR architecture foresees a universal middleware called SOME/IP that provides the service primitives, interfaces, and application protocols on top of Ethernet and IP. SOME/IP lacks a robust security architecture, even though security is an essential in future Internet-connected vehicles. In this paper, we augment the SOME/IP service discovery with an authentication and certificate management scheme based on DNSSEC and DANE. We argue that the deployment of well-proven, widely tested standard protocols should serve as an appropriate basis for a robust and reliable security infrastructure in cars. Our solution enables on-demand service authentication in offline scenarios, easy online updates, and remains free of attestation collisions. We evaluate our extension of the common vsomeip stack and find performance values that fully comply with car operations.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 12:01:19 GMT" } ]
2023-03-28T00:00:00
[ [ "Mueller", "Mehmet", "" ], [ "Häckel", "Timo", "" ], [ "Meyer", "Philipp", "" ], [ "Korf", "Franz", "" ], [ "Schmidt", "Thomas C.", "" ] ]
new_dataset
0.973641
2303.15166
Haoyuan Tian
Ran Yi, Haoyuan Tian, Zhihao Gu, Yu-Kun Lai and Paul L. Rosin
Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method
Accepted by CVPR 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Image aesthetics assessment (IAA) is a challenging task due to its highly subjective nature. Most of the current studies rely on large-scale datasets (e.g., AVA and AADB) to learn a general model for all kinds of photography images. However, little light has been shed on measuring the aesthetic quality of artistic images, and the existing datasets only contain relatively few artworks. Such a defect is a great obstacle to the aesthetic assessment of artistic images. To fill the gap in the field of artistic image aesthetics assessment (AIAA), we first introduce a large-scale AIAA dataset: Boldbrush Artistic Image Dataset (BAID), which consists of 60,337 artistic images covering various art forms, with more than 360,000 votes from online users. We then propose a new method, SAAN (Style-specific Art Assessment Network), which can effectively extract and utilize style-specific and generic aesthetic information to evaluate artistic images. Experiments demonstrate that our proposed approach outperforms existing IAA methods on the proposed BAID dataset according to quantitative comparisons. We believe the proposed dataset and method can serve as a foundation for future AIAA works and inspire more research in this field. Dataset and code are available at: https://github.com/Dreemurr-T/BAID.git
[ { "version": "v1", "created": "Mon, 27 Mar 2023 12:59:15 GMT" } ]
2023-03-28T00:00:00
[ [ "Yi", "Ran", "" ], [ "Tian", "Haoyuan", "" ], [ "Gu", "Zhihao", "" ], [ "Lai", "Yu-Kun", "" ], [ "Rosin", "Paul L.", "" ] ]
new_dataset
0.992654
2303.15187
Tommaso Lisini Baldi Dr.
Alberto Villani, Giovanni Cortigiani, Bernardo Brogi, Nicole D'Aurizio, Tommaso Lisini Baldi, and Domenico Prattichizzo
Avatarm: an Avatar With Manipulation Capabilities for the Physical Metaverse
null
null
null
null
cs.RO
http://creativecommons.org/licenses/by/4.0/
Metaverse is an immersive shared space that remote users can access through virtual and augmented reality interfaces, enabling their avatars to interact with each other and the surrounding. Although digital objects can be manipulated, physical objects cannot be touched, grasped, or moved within the metaverse due to the lack of a suitable interface. This work proposes a solution to overcome this limitation by introducing the concept of a Physical Metaverse enabled by a new interface named "Avatarm". The Avatarm consists in an avatar enhanced with a robotic arm that performs physical manipulation tasks while remaining entirely hidden in the metaverse. The users have the illusion that the avatar is directly manipulating objects without the mediation by a robot. The Avatarm is the first step towards a new metaverse, the "Physical Metaverse", where users can physically interact each other and with the environment.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 13:23:11 GMT" } ]
2023-03-28T00:00:00
[ [ "Villani", "Alberto", "" ], [ "Cortigiani", "Giovanni", "" ], [ "Brogi", "Bernardo", "" ], [ "D'Aurizio", "Nicole", "" ], [ "Baldi", "Tommaso Lisini", "" ], [ "Prattichizzo", "Domenico", "" ] ]
new_dataset
0.996025
2303.15193
Tarek Saier
Tarek Saier and Youxiang Dong and Michael F\"arber
CoCon: A Data Set on Combined Contextualized Research Artifact Use
submitted to JCDL2023
null
null
null
cs.DL cs.CL
http://creativecommons.org/licenses/by/4.0/
In the wake of information overload in academia, methodologies and systems for search, recommendation, and prediction to aid researchers in identifying relevant research are actively studied and developed. Existing work, however, is limited in terms of granularity, focusing only on the level of papers or a single type of artifact, such as data sets. To enable more holistic analyses and systems dealing with academic publications and their content, we propose CoCon, a large scholarly data set reflecting the combined use of research artifacts, contextualized in academic publications' full-text. Our data set comprises 35 k artifacts (data sets, methods, models, and tasks) and 340 k publications. We additionally formalize a link prediction task for "combined research artifact use prediction" and provide code to utilize analyses of and the development of ML applications on our data. All data and code is publicly available at https://github.com/IllDepence/contextgraph.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 13:29:09 GMT" } ]
2023-03-28T00:00:00
[ [ "Saier", "Tarek", "" ], [ "Dong", "Youxiang", "" ], [ "Färber", "Michael", "" ] ]
new_dataset
0.989756
2303.15306
Nicolas Lazzari
Nicolas Lazzari, Andrea Poltronieri, Valentina Presutti
Pitchclass2vec: Symbolic Music Structure Segmentation with Chord Embeddings
null
Proceedings of the 1st Workshop on Artificial Intelligence and Creativity co-located with 21th International Conference of the Italian Association for Artificial Intelligence(AIxIA 2022), Udine, Italy, November 28 - December 3, 2022
null
null
cs.SD cs.AI cs.LG eess.AS
http://creativecommons.org/licenses/by/4.0/
Structure perception is a fundamental aspect of music cognition in humans. Historically, the hierarchical organization of music into structures served as a narrative device for conveying meaning, creating expectancy, and evoking emotions in the listener. Thereby, musical structures play an essential role in music composition, as they shape the musical discourse through which the composer organises his ideas. In this paper, we present a novel music segmentation method, pitchclass2vec, based on symbolic chord annotations, which are embedded into continuous vector representations using both natural language processing techniques and custom-made encodings. Our algorithm is based on long-short term memory (LSTM) neural network and outperforms the state-of-the-art techniques based on symbolic chord annotations in the field.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 10:23:15 GMT" } ]
2023-03-28T00:00:00
[ [ "Lazzari", "Nicolas", "" ], [ "Poltronieri", "Andrea", "" ], [ "Presutti", "Valentina", "" ] ]
new_dataset
0.967363
2303.15334
Yifu Zhang
Yifu Zhang, Xinggang Wang, Xiaoqing Ye, Wei Zhang, Jincheng Lu, Xiao Tan, Errui Ding, Peize Sun, Jingdong Wang
ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box
Code is available at https://github.com/ifzhang/ByteTrack-V2. arXiv admin note: text overlap with arXiv:2110.06864; substantial text overlap with arXiv:2203.06424 by other authors
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects across video frames. Detection boxes serve as the basis of both 2D and 3D MOT. The inevitable changing of detection scores leads to object missing after tracking. We propose a hierarchical data association strategy to mine the true objects in low-score detection boxes, which alleviates the problems of object missing and fragmented trajectories. The simple and generic data association strategy shows effectiveness under both 2D and 3D settings. In 3D scenarios, it is much easier for the tracker to predict object velocities in the world coordinate. We propose a complementary motion prediction strategy that incorporates the detected velocities with a Kalman filter to address the problem of abrupt motion and short-term disappearing. ByteTrackV2 leads the nuScenes 3D MOT leaderboard in both camera (56.4% AMOTA) and LiDAR (70.1% AMOTA) modalities. Furthermore, it is nonparametric and can be integrated with various detectors, making it appealing in real applications. The source code is released at https://github.com/ifzhang/ByteTrack-V2.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 15:35:21 GMT" } ]
2023-03-28T00:00:00
[ [ "Zhang", "Yifu", "" ], [ "Wang", "Xinggang", "" ], [ "Ye", "Xiaoqing", "" ], [ "Zhang", "Wei", "" ], [ "Lu", "Jincheng", "" ], [ "Tan", "Xiao", "" ], [ "Ding", "Errui", "" ], [ "Sun", "Peize", "" ], [ "Wang", "Jingdong", "" ] ]
new_dataset
0.999326
2303.15352
Weimin Jin
Fengjiao Zou, Jennifer Ogle, Weimin Jin, Patrick Gerard, Daniel Petty, and Andrew Robb
Pedestrian Behavior Interacting with Autonomous Vehicles: Role of AV Operation and Signal Indication and Roadway Infrastructure
null
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Interacting with pedestrians is challenging for Autonomous vehicles (AVs). This study evaluates how AV operations /associated signaling and roadway infrastructure affect pedestrian behavior in virtual reality. AVs were designed with different operations and signal indications, including negotiating with no signal, negotiating with a yellow signal, and yellow/blue negotiating/no-yield indications. Results show that AV signal significantly impacts pedestrians' accepted gap, walking time, and waiting time. Pedestrians chose the largest open gap between cars with AV showing no signal, and had the slowest crossing speed with AV showing a yellow signal indication. Roadway infrastructure affects pedestrian walking time and waiting time.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 16:09:38 GMT" } ]
2023-03-28T00:00:00
[ [ "Zou", "Fengjiao", "" ], [ "Ogle", "Jennifer", "" ], [ "Jin", "Weimin", "" ], [ "Gerard", "Patrick", "" ], [ "Petty", "Daniel", "" ], [ "Robb", "Andrew", "" ] ]
new_dataset
0.9927
2303.15380
Chen Guo
Yifei Yin, Chen Guo, Manuel Kaufmann, Juan Jose Zarate, Jie Song, Otmar Hilliges
Hi4D: 4D Instance Segmentation of Close Human Interaction
Project page: https://yifeiyin04.github.io/Hi4D/
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We propose Hi4D, a method and dataset for the automatic analysis of physically close human-human interaction under prolonged contact. Robustly disentangling several in-contact subjects is a challenging task due to occlusions and complex shapes. Hence, existing multi-view systems typically fuse 3D surfaces of close subjects into a single, connected mesh. To address this issue we leverage i) individually fitted neural implicit avatars; ii) an alternating optimization scheme that refines pose and surface through periods of close proximity; and iii) thus segment the fused raw scans into individual instances. From these instances we compile Hi4D dataset of 4D textured scans of 20 subject pairs, 100 sequences, and a total of more than 11K frames. Hi4D contains rich interaction-centric annotations in 2D and 3D alongside accurately registered parametric body models. We define varied human pose and shape estimation tasks on this dataset and provide results from state-of-the-art methods on these benchmarks.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 16:53:09 GMT" } ]
2023-03-28T00:00:00
[ [ "Yin", "Yifei", "" ], [ "Guo", "Chen", "" ], [ "Kaufmann", "Manuel", "" ], [ "Zarate", "Juan Jose", "" ], [ "Song", "Jie", "" ], [ "Hilliges", "Otmar", "" ] ]
new_dataset
0.996085
2303.15417
Jaeha Kim
Yeonguk Oh, JoonKyu Park, Jaeha Kim, Gyeongsik Moon, Kyoung Mu Lee
Recovering 3D Hand Mesh Sequence from a Single Blurry Image: A New Dataset and Temporal Unfolding
Accepted at CVPR 2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by-nc-sa/4.0/
Hands, one of the most dynamic parts of our body, suffer from blur due to their active movements. However, previous 3D hand mesh recovery methods have mainly focused on sharp hand images rather than considering blur due to the absence of datasets providing blurry hand images. We first present a novel dataset BlurHand, which contains blurry hand images with 3D groundtruths. The BlurHand is constructed by synthesizing motion blur from sequential sharp hand images, imitating realistic and natural motion blurs. In addition to the new dataset, we propose BlurHandNet, a baseline network for accurate 3D hand mesh recovery from a blurry hand image. Our BlurHandNet unfolds a blurry input image to a 3D hand mesh sequence to utilize temporal information in the blurry input image, while previous works output a static single hand mesh. We demonstrate the usefulness of BlurHand for the 3D hand mesh recovery from blurry images in our experiments. The proposed BlurHandNet produces much more robust results on blurry images while generalizing well to in-the-wild images. The training codes and BlurHand dataset are available at https://github.com/JaehaKim97/BlurHand_RELEASE.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 17:40:29 GMT" } ]
2023-03-28T00:00:00
[ [ "Oh", "Yeonguk", "" ], [ "Park", "JoonKyu", "" ], [ "Kim", "Jaeha", "" ], [ "Moon", "Gyeongsik", "" ], [ "Lee", "Kyoung Mu", "" ] ]
new_dataset
0.998778
2303.15433
Hao Phung
Thanh Van Le, Hao Phung, Thuan Hoang Nguyen, Quan Dao, Ngoc Tran, Anh Tran
Anti-DreamBooth: Protecting users from personalized text-to-image synthesis
Project page: https://anti-dreambooth.github.io/
null
null
null
cs.CV cs.CR cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Text-to-image diffusion models are nothing but a revolution, allowing anyone, even without design skills, to create realistic images from simple text inputs. With powerful personalization tools like DreamBooth, they can generate images of a specific person just by learning from his/her few reference images. However, when misused, such a powerful and convenient tool can produce fake news or disturbing content targeting any individual victim, posing a severe negative social impact. In this paper, we explore a defense system called Anti-DreamBooth against such malicious use of DreamBooth. The system aims to add subtle noise perturbation to each user's image before publishing in order to disrupt the generation quality of any DreamBooth model trained on these perturbed images. We investigate a wide range of algorithms for perturbation optimization and extensively evaluate them on two facial datasets over various text-to-image model versions. Despite the complicated formulation of DreamBooth and Diffusion-based text-to-image models, our methods effectively defend users from the malicious use of those models. Their effectiveness withstands even adverse conditions, such as model or prompt/term mismatching between training and testing. Our code will be available at \href{https://github.com/VinAIResearch/Anti-DreamBooth.git}{https://github.com/VinAIResearch/Anti-DreamBooth.git}.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 17:55:44 GMT" } ]
2023-03-28T00:00:00
[ [ "Van Le", "Thanh", "" ], [ "Phung", "Hao", "" ], [ "Nguyen", "Thuan Hoang", "" ], [ "Dao", "Quan", "" ], [ "Tran", "Ngoc", "" ], [ "Tran", "Anh", "" ] ]
new_dataset
0.995825
2303.15437
Anurag Ranjan
Anurag Ranjan, Kwang Moo Yi, Jen-Hao Rick Chang, Oncel Tuzel
FaceLit: Neural 3D Relightable Faces
CVPR 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We propose a generative framework, FaceLit, capable of generating a 3D face that can be rendered at various user-defined lighting conditions and views, learned purely from 2D images in-the-wild without any manual annotation. Unlike existing works that require careful capture setup or human labor, we rely on off-the-shelf pose and illumination estimators. With these estimates, we incorporate the Phong reflectance model in the neural volume rendering framework. Our model learns to generate shape and material properties of a face such that, when rendered according to the natural statistics of pose and illumination, produces photorealistic face images with multiview 3D and illumination consistency. Our method enables photorealistic generation of faces with explicit illumination and view controls on multiple datasets - FFHQ, MetFaces and CelebA-HQ. We show state-of-the-art photorealism among 3D aware GANs on FFHQ dataset achieving an FID score of 3.5.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 17:59:10 GMT" } ]
2023-03-28T00:00:00
[ [ "Ranjan", "Anurag", "" ], [ "Yi", "Kwang Moo", "" ], [ "Chang", "Jen-Hao Rick", "" ], [ "Tuzel", "Oncel", "" ] ]
new_dataset
0.997258
2303.15443
Tarun Kalluri
Tarun Kalluri, Wangdong Xu, Manmohan Chandraker
GeoNet: Benchmarking Unsupervised Adaptation across Geographies
CVPR 2023 Camera Ready. Project Page: https://tarun005.github.io/GeoNet
null
null
null
cs.CV cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
In recent years, several efforts have been aimed at improving the robustness of vision models to domains and environments unseen during training. An important practical problem pertains to models deployed in a new geography that is under-represented in the training dataset, posing a direct challenge to fair and inclusive computer vision. In this paper, we study the problem of geographic robustness and make three main contributions. First, we introduce a large-scale dataset GeoNet for geographic adaptation containing benchmarks across diverse tasks like scene recognition (GeoPlaces), image classification (GeoImNet) and universal adaptation (GeoUniDA). Second, we investigate the nature of distribution shifts typical to the problem of geographic adaptation and hypothesize that the major source of domain shifts arise from significant variations in scene context (context shift), object design (design shift) and label distribution (prior shift) across geographies. Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures on GeoNet, showing that they do not suffice for geographical adaptation, and that large-scale pre-training using large vision models also does not lead to geographic robustness. Our dataset is publicly available at https://tarun005.github.io/GeoNet.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 17:59:34 GMT" } ]
2023-03-28T00:00:00
[ [ "Kalluri", "Tarun", "" ], [ "Xu", "Wangdong", "" ], [ "Chandraker", "Manmohan", "" ] ]
new_dataset
0.999579
2303.15445
Ron Yosef
Ron Yosef, Yonatan Bitton, Dafna Shahaf
IRFL: Image Recognition of Figurative Language
null
null
null
null
cs.CL cs.AI cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Figures of speech such as metaphors, similes, and idioms allow language to be expressive, invoke emotion, and communicate abstract ideas that might otherwise be difficult to visualize. These figurative forms are often conveyed through multiple modes, such as text and images, and frequently appear in advertising, news, social media, etc. Understanding multimodal figurative language is an essential component of human communication, and it plays a significant role in our daily interactions. While humans can intuitively understand multimodal figurative language, this poses a challenging task for machines that requires the cognitive ability to map between domains, abstraction, commonsense, and profound language and cultural knowledge. In this work, we propose the Image Recognition of Figurative Language dataset to examine vision and language models' understanding of figurative language. We leverage human annotation and an automatic pipeline we created to generate a multimodal dataset and introduce two novel tasks as a benchmark for multimodal figurative understanding. We experiment with several baseline models and find that all perform substantially worse than humans. We hope our dataset and benchmark will drive the development of models that will better understand figurative language.
[ { "version": "v1", "created": "Mon, 27 Mar 2023 17:59:55 GMT" } ]
2023-03-28T00:00:00
[ [ "Yosef", "Ron", "" ], [ "Bitton", "Yonatan", "" ], [ "Shahaf", "Dafna", "" ] ]
new_dataset
0.999842
2202.04278
David Naumann
Timos Antonopoulos, Eric Koskinen, Ton Chanh Le, Ramana Nagasamudram, David A. Naumann, Minh Ngo
An algebra of alignment for relational verification
v2 adds examples and an undecidability result; v3 has expository improvements (POPL version + appendix); v4 fixes the proof of Thm 4.3
null
10.1145/3571213
null
cs.LO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Relational verification encompasses information flow security, regression verification, translation validation for compilers, and more. Effective alignment of the programs and computations to be related facilitates use of simpler relational invariants and relational procedure specs, which in turn enables automation and modular reasoning. Alignment has been explored in terms of trace pairs, deductive rules of relational Hoare logics (RHL), and several forms of product automata. This article shows how a simple extension of Kleene Algebra with Tests (KAT), called BiKAT, subsumes prior formulations, including alignment witnesses for forall-exists properties, which brings to light new RHL-style rules for such properties. Alignments can be discovered algorithmically or devised manually but, in either case, their adequacy with respect to the original programs must be proved; an explicit algebra enables constructive proof by equational reasoning. Furthermore our approach inherits algorithmic benefits from existing KAT-based techniques and tools, which are applicable to a range of semantic models.
[ { "version": "v1", "created": "Wed, 9 Feb 2022 04:53:04 GMT" }, { "version": "v2", "created": "Sat, 9 Jul 2022 21:14:46 GMT" }, { "version": "v3", "created": "Wed, 7 Dec 2022 02:34:16 GMT" }, { "version": "v4", "created": "Thu, 23 Mar 2023 18:05:57 GMT" } ]
2023-03-27T00:00:00
[ [ "Antonopoulos", "Timos", "" ], [ "Koskinen", "Eric", "" ], [ "Le", "Ton Chanh", "" ], [ "Nagasamudram", "Ramana", "" ], [ "Naumann", "David A.", "" ], [ "Ngo", "Minh", "" ] ]
new_dataset
0.957154
2207.10660
Garrick Brazil
Garrick Brazil, Abhinav Kumar, Julian Straub, Nikhila Ravi, Justin Johnson, Georgia Gkioxari
Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
CVPR 2023, Project website: https://omni3d.garrickbrazil.com/
null
null
null
cs.CV
http://creativecommons.org/licenses/by-nc-sa/4.0/
Recognizing scenes and objects in 3D from a single image is a longstanding goal of computer vision with applications in robotics and AR/VR. For 2D recognition, large datasets and scalable solutions have led to unprecedented advances. In 3D, existing benchmarks are small in size and approaches specialize in few object categories and specific domains, e.g. urban driving scenes. Motivated by the success of 2D recognition, we revisit the task of 3D object detection by introducing a large benchmark, called Omni3D. Omni3D re-purposes and combines existing datasets resulting in 234k images annotated with more than 3 million instances and 98 categories. 3D detection at such scale is challenging due to variations in camera intrinsics and the rich diversity of scene and object types. We propose a model, called Cube R-CNN, designed to generalize across camera and scene types with a unified approach. We show that Cube R-CNN outperforms prior works on the larger Omni3D and existing benchmarks. Finally, we prove that Omni3D is a powerful dataset for 3D object recognition and show that it improves single-dataset performance and can accelerate learning on new smaller datasets via pre-training.
[ { "version": "v1", "created": "Thu, 21 Jul 2022 17:56:22 GMT" }, { "version": "v2", "created": "Fri, 24 Mar 2023 00:42:18 GMT" } ]
2023-03-27T00:00:00
[ [ "Brazil", "Garrick", "" ], [ "Kumar", "Abhinav", "" ], [ "Straub", "Julian", "" ], [ "Ravi", "Nikhila", "" ], [ "Johnson", "Justin", "" ], [ "Gkioxari", "Georgia", "" ] ]
new_dataset
0.999868
2211.13874
Haoran Bai
Haoran Bai, Di Kang, Haoxian Zhang, Jinshan Pan, Linchao Bao
FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction
The dataset, code, and pre-trained texture decoder are publicly available at https://github.com/csbhr/FFHQ-UV
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present a large-scale facial UV-texture dataset that contains over 50,000 high-quality texture UV-maps with even illuminations, neutral expressions, and cleaned facial regions, which are desired characteristics for rendering realistic 3D face models under different lighting conditions. The dataset is derived from a large-scale face image dataset namely FFHQ, with the help of our fully automatic and robust UV-texture production pipeline. Our pipeline utilizes the recent advances in StyleGAN-based facial image editing approaches to generate multi-view normalized face images from single-image inputs. An elaborated UV-texture extraction, correction, and completion procedure is then applied to produce high-quality UV-maps from the normalized face images. Compared with existing UV-texture datasets, our dataset has more diverse and higher-quality texture maps. We further train a GAN-based texture decoder as the nonlinear texture basis for parametric fitting based 3D face reconstruction. Experiments show that our method improves the reconstruction accuracy over state-of-the-art approaches, and more importantly, produces high-quality texture maps that are ready for realistic renderings. The dataset, code, and pre-trained texture decoder are publicly available at https://github.com/csbhr/FFHQ-UV.
[ { "version": "v1", "created": "Fri, 25 Nov 2022 03:21:05 GMT" }, { "version": "v2", "created": "Fri, 24 Mar 2023 14:44:50 GMT" } ]
2023-03-27T00:00:00
[ [ "Bai", "Haoran", "" ], [ "Kang", "Di", "" ], [ "Zhang", "Haoxian", "" ], [ "Pan", "Jinshan", "" ], [ "Bao", "Linchao", "" ] ]
new_dataset
0.999798
2211.14306
Mehdi S. M. Sajjadi
Mehdi S. M. Sajjadi, Aravindh Mahendran, Thomas Kipf, Etienne Pot, Daniel Duckworth, Mario Lucic, Klaus Greff
RUST: Latent Neural Scene Representations from Unposed Imagery
CVPR 2023 Highlight. Project website: https://rust-paper.github.io/
null
null
null
cs.CV cs.GR cs.LG eess.IV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Inferring the structure of 3D scenes from 2D observations is a fundamental challenge in computer vision. Recently popularized approaches based on neural scene representations have achieved tremendous impact and have been applied across a variety of applications. One of the major remaining challenges in this space is training a single model which can provide latent representations which effectively generalize beyond a single scene. Scene Representation Transformer (SRT) has shown promise in this direction, but scaling it to a larger set of diverse scenes is challenging and necessitates accurately posed ground truth data. To address this problem, we propose RUST (Really Unposed Scene representation Transformer), a pose-free approach to novel view synthesis trained on RGB images alone. Our main insight is that one can train a Pose Encoder that peeks at the target image and learns a latent pose embedding which is used by the decoder for view synthesis. We perform an empirical investigation into the learned latent pose structure and show that it allows meaningful test-time camera transformations and accurate explicit pose readouts. Perhaps surprisingly, RUST achieves similar quality as methods which have access to perfect camera pose, thereby unlocking the potential for large-scale training of amortized neural scene representations.
[ { "version": "v1", "created": "Fri, 25 Nov 2022 18:59:10 GMT" }, { "version": "v2", "created": "Fri, 24 Mar 2023 16:56:25 GMT" } ]
2023-03-27T00:00:00
[ [ "Sajjadi", "Mehdi S. M.", "" ], [ "Mahendran", "Aravindh", "" ], [ "Kipf", "Thomas", "" ], [ "Pot", "Etienne", "" ], [ "Duckworth", "Daniel", "" ], [ "Lucic", "Mario", "" ], [ "Greff", "Klaus", "" ] ]
new_dataset
0.987096
2212.08013
Lucas Beyer
Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic
FlexiViT: One Model for All Patch Sizes
Code and pre-trained models available at https://github.com/google-research/big_vision. All authors made significant technical contributions. CVPR 2023
null
null
null
cs.CV cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
Vision Transformers convert images to sequences by slicing them into patches. The size of these patches controls a speed/accuracy tradeoff, with smaller patches leading to higher accuracy at greater computational cost, but changing the patch size typically requires retraining the model. In this paper, we demonstrate that simply randomizing the patch size at training time leads to a single set of weights that performs well across a wide range of patch sizes, making it possible to tailor the model to different compute budgets at deployment time. We extensively evaluate the resulting model, which we call FlexiViT, on a wide range of tasks, including classification, image-text retrieval, open-world detection, panoptic segmentation, and semantic segmentation, concluding that it usually matches, and sometimes outperforms, standard ViT models trained at a single patch size in an otherwise identical setup. Hence, FlexiViT training is a simple drop-in improvement for ViT that makes it easy to add compute-adaptive capabilities to most models relying on a ViT backbone architecture. Code and pre-trained models are available at https://github.com/google-research/big_vision
[ { "version": "v1", "created": "Thu, 15 Dec 2022 18:18:38 GMT" }, { "version": "v2", "created": "Thu, 23 Mar 2023 21:38:16 GMT" } ]
2023-03-27T00:00:00
[ [ "Beyer", "Lucas", "" ], [ "Izmailov", "Pavel", "" ], [ "Kolesnikov", "Alexander", "" ], [ "Caron", "Mathilde", "" ], [ "Kornblith", "Simon", "" ], [ "Zhai", "Xiaohua", "" ], [ "Minderer", "Matthias", "" ], [ "Tschannen", "Michael", "" ], [ "Alabdulmohsin", "Ibrahim", "" ], [ "Pavetic", "Filip", "" ] ]
new_dataset
0.998035
2212.09877
Ning Yu
Ning Yu, Chia-Chih Chen, Zeyuan Chen, Rui Meng, Gang Wu, Paul Josel, Juan Carlos Niebles, Caiming Xiong, Ran Xu
LayoutDETR: Detection Transformer Is a Good Multimodal Layout Designer
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Graphic layout designs play an essential role in visual communication. Yet handcrafting layout designs is skill-demanding, time-consuming, and non-scalable to batch production. Generative models emerge to make design automation scalable but it remains non-trivial to produce designs that comply with designers' multimodal desires, i.e., constrained by background images and driven by foreground content. We propose LayoutDETR that inherits the high quality and realism from generative modeling, while reformulating content-aware requirements as a detection problem: we learn to detect in a background image the reasonable locations, scales, and spatial relations for multimodal foreground elements in a layout. Our solution sets a new state-of-the-art performance for layout generation on public benchmarks and on our newly-curated ad banner dataset. We integrate our solution into a graphical system that facilitates user studies, and show that users prefer our designs over baselines by significant margins. Our code, models, dataset, graphical system, and demos are available at https://github.com/salesforce/LayoutDETR.
[ { "version": "v1", "created": "Mon, 19 Dec 2022 21:57:35 GMT" }, { "version": "v2", "created": "Mon, 30 Jan 2023 07:57:53 GMT" }, { "version": "v3", "created": "Fri, 24 Mar 2023 08:56:44 GMT" } ]
2023-03-27T00:00:00
[ [ "Yu", "Ning", "" ], [ "Chen", "Chia-Chih", "" ], [ "Chen", "Zeyuan", "" ], [ "Meng", "Rui", "" ], [ "Wu", "Gang", "" ], [ "Josel", "Paul", "" ], [ "Niebles", "Juan Carlos", "" ], [ "Xiong", "Caiming", "" ], [ "Xu", "Ran", "" ] ]
new_dataset
0.993971
2301.13760
Pascal Nasahl
Pascal Nasahl, Salmin Sultana, Hans Liljestrand, Karanvir Grewal, Michael LeMay, David M. Durham, David Schrammel, Stefan Mangard
EC-CFI: Control-Flow Integrity via Code Encryption Counteracting Fault Attacks
Accepted at HOST'23
null
null
null
cs.CR
http://creativecommons.org/licenses/by/4.0/
Fault attacks enable adversaries to manipulate the control-flow of security-critical applications. By inducing targeted faults into the CPU, the software's call graph can be escaped and the control-flow can be redirected to arbitrary functions inside the program. To protect the control-flow from these attacks, dedicated fault control-flow integrity (CFI) countermeasures are commonly deployed. However, these schemes either have high detection latencies or require intrusive hardware changes. In this paper, we present EC-CFI, a software-based cryptographically enforced CFI scheme with no detection latency utilizing hardware features of recent Intel platforms. Our EC-CFI prototype is designed to prevent an adversary from escaping the program's call graph using faults by encrypting each function with a different key before execution. At runtime, the instrumented program dynamically derives the decryption key, ensuring that the code only can be successfully decrypted when the program follows the intended call graph. To enable this level of protection on Intel commodity systems, we introduce extended page table (EPT) aliasing allowing us to achieve function-granular encryption by combing Intel's TME-MK and virtualization technology. We open-source our custom LLVM-based toolchain automatically protecting arbitrary programs with EC-CFI. Furthermore, we evaluate our EPT aliasing approach with the SPEC CPU2017 and Embench-IoT benchmarks and discuss and evaluate potential TME-MK hardware changes minimizing runtime overheads.
[ { "version": "v1", "created": "Tue, 31 Jan 2023 16:51:33 GMT" }, { "version": "v2", "created": "Fri, 24 Mar 2023 10:41:21 GMT" } ]
2023-03-27T00:00:00
[ [ "Nasahl", "Pascal", "" ], [ "Sultana", "Salmin", "" ], [ "Liljestrand", "Hans", "" ], [ "Grewal", "Karanvir", "" ], [ "LeMay", "Michael", "" ], [ "Durham", "David M.", "" ], [ "Schrammel", "David", "" ], [ "Mangard", "Stefan", "" ] ]
new_dataset
0.998917
2303.02416
Yuan Liu
Yuan Liu, Songyang Zhang, Jiacheng Chen, Kai Chen, Dahua Lin
PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling
Update code link and add additional results
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Masked Image Modeling (MIM) has achieved promising progress with the advent of Masked Autoencoders (MAE) and BEiT. However, subsequent works have complicated the framework with new auxiliary tasks or extra pre-trained models, inevitably increasing computational overhead. This paper undertakes a fundamental analysis of MIM from the perspective of pixel reconstruction, which examines the input image patches and reconstruction target, and highlights two critical but previously overlooked bottlenecks. Based on this analysis, we propose a remarkably simple and effective method, {\ourmethod}, that entails two strategies: 1) filtering the high-frequency components from the reconstruction target to de-emphasize the network's focus on texture-rich details and 2) adopting a conservative data transform strategy to alleviate the problem of missing foreground in MIM training. {\ourmethod} can be easily integrated into most existing pixel-based MIM approaches (\ie, using raw images as reconstruction target) with negligible additional computation. Without bells and whistles, our method consistently improves three MIM approaches, MAE, ConvMAE, and LSMAE, across various downstream tasks. We believe this effective plug-and-play method will serve as a strong baseline for self-supervised learning and provide insights for future improvements of the MIM framework. Code and models are available at \url{https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/pixmim}.
[ { "version": "v1", "created": "Sat, 4 Mar 2023 13:38:51 GMT" }, { "version": "v2", "created": "Fri, 24 Mar 2023 05:37:41 GMT" } ]
2023-03-27T00:00:00
[ [ "Liu", "Yuan", "" ], [ "Zhang", "Songyang", "" ], [ "Chen", "Jiacheng", "" ], [ "Chen", "Kai", "" ], [ "Lin", "Dahua", "" ] ]
new_dataset
0.953138
2303.03711
Pascal Nasahl
Pascal Nasahl and Stefan Mangard
SCRAMBLE-CFI: Mitigating Fault-Induced Control-Flow Attacks on OpenTitan
Accepted at GLSVLSI'23
null
null
null
cs.CR
http://creativecommons.org/licenses/by/4.0/
Secure elements physically exposed to adversaries are frequently targeted by fault attacks. These attacks can be utilized to hijack the control-flow of software allowing the attacker to bypass security measures, extract sensitive data, or gain full code execution. In this paper, we systematically analyze the threat vector of fault-induced control-flow manipulations on the open-source OpenTitan secure element. Our thorough analysis reveals that current countermeasures of this chip either induce large area overheads or still cannot prevent the attacker from exploiting the identified threats. In this context, we introduce SCRAMBLE-CFI, an encryption-based control-flow integrity scheme utilizing existing hardware features of OpenTitan. SCRAMBLE-CFI confines, with minimal hardware overhead, the impact of fault-induced control-flow attacks by encrypting each function with a different encryption tweak at load-time. At runtime, code only can be successfully decrypted when the correct decryption tweak is active. We open-source our hardware changes and release our LLVM toolchain automatically protecting programs. Our analysis shows that SCRAMBLE-CFI complementarily enhances security guarantees of OpenTitan with a negligible hardware overhead of less than 3.97 % and a runtime overhead of 7.02 % for the Embench-IoT benchmarks.
[ { "version": "v1", "created": "Tue, 7 Mar 2023 07:53:02 GMT" }, { "version": "v2", "created": "Wed, 22 Mar 2023 12:05:50 GMT" }, { "version": "v3", "created": "Fri, 24 Mar 2023 10:30:09 GMT" } ]
2023-03-27T00:00:00
[ [ "Nasahl", "Pascal", "" ], [ "Mangard", "Stefan", "" ] ]
new_dataset
0.99598
2303.07541
Mina Rezaei
Mina Rezaei and Patsy Eubanks Owens
Young Humans Make Change, Young Users Click: Creating Youth-Centered Networked Social Movements
null
CHI 2023 Workshop titled "Supporting Social Movements Through HCI and Design"
null
null
cs.HC
http://creativecommons.org/licenses/by/4.0/
From the urbanists' perspective, the everyday experience of young people, as an underrepresented group in the design of public spaces, includes tactics they use to challenge the strategies which rule over urban spaces. In this regard, youth led social movements are a set of collective tactics which groups of young people use to resist power structures. Social informational streams have revolutionized the way youth organize and mobilize for social movements throughout the world, especially in urban areas. However, just like public spaces, these algorithm based platforms have been developed with a great power imbalance between the developers and users which results in the creation of non inclusive social informational streams for young activists. Social activism grows agency and confidence in youth which is critical to their development. This paper employs a youth centric lens, which is used in designing public spaces, for designing algorithmic spaces that can improve bottom up youth led movements. By reviewing the structure of these spaces and how young people interact with these structures in the different cultural contexts of Iran and the US, we propose a humanistic approach to designing social informational streams which can enhance youth activism.
[ { "version": "v1", "created": "Tue, 14 Mar 2023 00:07:43 GMT" }, { "version": "v2", "created": "Thu, 23 Mar 2023 21:16:54 GMT" } ]
2023-03-27T00:00:00
[ [ "Rezaei", "Mina", "" ], [ "Owens", "Patsy Eubanks", "" ] ]
new_dataset
0.958899
2303.10795
Vaibhav Garg
Vaibhav Garg, Hui Guo, Nirav Ajmeri, Saikath Bhattacharya, and Munindar P. Singh
iRogue: Identifying Rogue Behavior from App Reviews
null
null
null
null
cs.CR
http://creativecommons.org/licenses/by/4.0/
An app user can access information of other users or third parties. We define rogue mobile apps as those that enable a user (abuser) to access information of another user or third party (victim), in a way that violates the victim's privacy expectations. Such apps are dual-use and their identification is nontrivial. We propose iRogue, an approach for identifying rogue apps based on their reviews, posted by victims, abusers, and others. iRogue involves training on deep learning features extracted from their 1,884 manually labeled reviews. iRogue first identifies how alarming a review is with respect to rogue behavior and, second, generates a rogue score for an app. iRogue predicts 100 rogue apps from a seed dataset curated following a previous study. Also, iRogue examines apps in other datasets of scraped reviews, and predicts an additional 139 rogue apps. On labeled ground truth, iRogue achieves the highest recall, and outperforms baseline approaches that leverage app descriptions and reviews. A qualitative analysis of alarming reviews reveals rogue functionalities. App users, platforms, and developers should be aware of such apps and their functionalities and take measures to curb privacy risk.
[ { "version": "v1", "created": "Sun, 19 Mar 2023 23:43:36 GMT" } ]
2023-03-27T00:00:00
[ [ "Garg", "Vaibhav", "" ], [ "Guo", "Hui", "" ], [ "Ajmeri", "Nirav", "" ], [ "Bhattacharya", "Saikath", "" ], [ "Singh", "Munindar P.", "" ] ]
new_dataset
0.988122
2303.11972
Pedro Hecht
Juan Pedro Hecht, Hugo Daniel Scolnik
A Post Quantum Key Agreement Protocol Based on a Modified Matrix Power Function over a Rectangular Matrices Semiring
6 pages, 20 references
null
null
null
cs.CR
http://creativecommons.org/licenses/by-nc-nd/4.0/
We present an improved post quantum version of Sakalauskas matrix power function key agreement protocol, using rectangular matrices instead of the original square ones. Sakalauskas matrix power function is an efficient and secure way to generate a shared secret key, and using rectangular matrices provides additional flexibility and security. This method reduces the computational burden by allowing smaller random integer matrices while maintaining equal security. Another advantage of using the rank deficient rectangular matrices over key agreement protocols is that it blocks linearization attacks.
[ { "version": "v1", "created": "Tue, 21 Mar 2023 16:07:17 GMT" }, { "version": "v2", "created": "Wed, 22 Mar 2023 15:45:25 GMT" }, { "version": "v3", "created": "Thu, 23 Mar 2023 17:27:27 GMT" }, { "version": "v4", "created": "Fri, 24 Mar 2023 17:45:59 GMT" } ]
2023-03-27T00:00:00
[ [ "Hecht", "Juan Pedro", "" ], [ "Scolnik", "Hugo Daniel", "" ] ]
new_dataset
0.995827
2303.12564
Zhongjin Luo
Zhongjin Luo, Shengcai Cai, Jinguo Dong, Ruibo Ming, Liangdong Qiu, Xiaohang Zhan, Xiaoguang Han
RaBit: Parametric Modeling of 3D Biped Cartoon Characters with a Topological-consistent Dataset
CVPR 2023, Project page: https://gaplab.cuhk.edu.cn/projects/RaBit/
null
null
null
cs.CV
http://creativecommons.org/publicdomain/zero/1.0/
Assisting people in efficiently producing visually plausible 3D characters has always been a fundamental research topic in computer vision and computer graphics. Recent learning-based approaches have achieved unprecedented accuracy and efficiency in the area of 3D real human digitization. However, none of the prior works focus on modeling 3D biped cartoon characters, which are also in great demand in gaming and filming. In this paper, we introduce 3DBiCar, the first large-scale dataset of 3D biped cartoon characters, and RaBit, the corresponding parametric model. Our dataset contains 1,500 topologically consistent high-quality 3D textured models which are manually crafted by professional artists. Built upon the data, RaBit is thus designed with a SMPL-like linear blend shape model and a StyleGAN-based neural UV-texture generator, simultaneously expressing the shape, pose, and texture. To demonstrate the practicality of 3DBiCar and RaBit, various applications are conducted, including single-view reconstruction, sketch-based modeling, and 3D cartoon animation. For the single-view reconstruction setting, we find a straightforward global mapping from input images to the output UV-based texture maps tends to lose detailed appearances of some local parts (e.g., nose, ears). Thus, a part-sensitive texture reasoner is adopted to make all important local areas perceived. Experiments further demonstrate the effectiveness of our method both qualitatively and quantitatively. 3DBiCar and RaBit are available at gaplab.cuhk.edu.cn/projects/RaBit.
[ { "version": "v1", "created": "Wed, 22 Mar 2023 13:46:15 GMT" }, { "version": "v2", "created": "Fri, 24 Mar 2023 07:49:32 GMT" } ]
2023-03-27T00:00:00
[ [ "Luo", "Zhongjin", "" ], [ "Cai", "Shengcai", "" ], [ "Dong", "Jinguo", "" ], [ "Ming", "Ruibo", "" ], [ "Qiu", "Liangdong", "" ], [ "Zhan", "Xiaohang", "" ], [ "Han", "Xiaoguang", "" ] ]
new_dataset
0.999718
2303.12968
Tim Scargill
Tim Scargill, Sangjun Eom, Ying Chen, Maria Gorlatova
Ambient Intelligence for Next-Generation AR
This is a preprint of a book chapter which will appear in the Springer Handbook of the Metaverse
null
null
null
cs.HC
http://creativecommons.org/licenses/by/4.0/
Next-generation augmented reality (AR) promises a high degree of context-awareness - a detailed knowledge of the environmental, user, social and system conditions in which an AR experience takes place. This will facilitate both the closer integration of the real and virtual worlds, and the provision of context-specific content or adaptations. However, environmental awareness in particular is challenging to achieve using AR devices alone; not only are these mobile devices' view of an environment spatially and temporally limited, but the data obtained by onboard sensors is frequently inaccurate and incomplete. This, combined with the fact that many aspects of core AR functionality and user experiences are impacted by properties of the real environment, motivates the use of ambient IoT devices, wireless sensors and actuators placed in the surrounding environment, for the measurement and optimization of environment properties. In this book chapter we categorize and examine the wide variety of ways in which these IoT sensors and actuators can support or enhance AR experiences, including quantitative insights and proof-of-concept systems that will inform the development of future solutions. We outline the challenges and opportunities associated with several important research directions which must be addressed to realize the full potential of next-generation AR.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 00:25:08 GMT" }, { "version": "v2", "created": "Fri, 24 Mar 2023 14:09:40 GMT" } ]
2023-03-27T00:00:00
[ [ "Scargill", "Tim", "" ], [ "Eom", "Sangjun", "" ], [ "Chen", "Ying", "" ], [ "Gorlatova", "Maria", "" ] ]
new_dataset
0.998943
2303.13522
Tallulah Frappier
Tallulah Frappier (I3, CESSP)
Online Assemblies: Civic Technologies Reshaping the Public Space
null
null
null
null
cs.HC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Speaking or writing of political assemblies tends to evoke the action of people gathering to deliberate, or the spaces in which this deliberation might take place. One thing that is often overlooked, however, is the fact that these spaces can be digital. Online assemblies have become more widespread in recent years; from the first Web forums to civic technologies specifically designed to host collective political debates. As digital services affect our possibilities for political mobilization and participation, I will here attempt to define the qualities specific to online assemblies, and to identify several patterns and continuities in the design features of civic technologies offering online spaces for debate.
[ { "version": "v1", "created": "Mon, 30 Jan 2023 15:02:43 GMT" } ]
2023-03-27T00:00:00
[ [ "Frappier", "Tallulah", "", "I3, CESSP" ] ]
new_dataset
0.975496
2303.13524
Filipo Sharevski
Filipo Sharevski and Jennifer Vander Loop and Peter Jachim and Amy Devine and Emma Pieroni
Talking Abortion (Mis)information with ChatGPT on TikTok
null
null
null
null
cs.HC cs.CY cs.SI
http://creativecommons.org/licenses/by/4.0/
In this study, we tested users' perception of accuracy and engagement with TikTok videos in which ChatGPT responded to prompts about "at-home" abortion remedies. The chatbot's responses, though somewhat vague and confusing, nonetheless recommended consulting with health professionals before attempting an "at-home" abortion. We used ChatGPT to create two TikTok video variants - one where users can see ChatGPT explicitly typing back a response, and one where the text response is presented without any notion to the chatbot. We randomly exposed 100 participants to each variant and found that the group of participants unaware of ChatGPT's text synthetization was more inclined to believe the responses were misinformation. Under the same impression, TikTok itself attached misinformation warning labels ("Get the facts about abortion") to all videos after we collected our initial results. We then decided to test the videos again with another set of 50 participants and found that the labels did not affect the perceptions of abortion misinformation except in the case where ChatGPT explicitly responded to a prompt for a lyrical output. We also found that more than 60% of the participants expressed negative or hesitant opinions about chatbots as sources of credible health information.
[ { "version": "v1", "created": "Thu, 23 Feb 2023 17:35:27 GMT" } ]
2023-03-27T00:00:00
[ [ "Sharevski", "Filipo", "" ], [ "Loop", "Jennifer Vander", "" ], [ "Jachim", "Peter", "" ], [ "Devine", "Amy", "" ], [ "Pieroni", "Emma", "" ] ]
new_dataset
0.976715
2303.13527
Yuyang Wang
Yuyang Wang, Ruichen Li, Jean-R\'emy Chardonnet, Pan Hui
Dataset for predicting cybersickness from a virtual navigation task
null
null
null
null
cs.HC cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This work presents a dataset collected to predict cybersickness in virtual reality environments. The data was collected from navigation tasks in a virtual environment designed to induce cybersickness. The dataset consists of many data points collected from diverse participants, including physiological responses (EDA and Heart Rate) and self-reported cybersickness symptoms. The paper will provide a detailed description of the dataset, including the arranged navigation task, the data collection procedures, and the data format. The dataset will serve as a valuable resource for researchers to develop and evaluate predictive models for cybersickness and will facilitate more research in cybersickness mitigation.
[ { "version": "v1", "created": "Tue, 7 Feb 2023 03:57:56 GMT" } ]
2023-03-27T00:00:00
[ [ "Wang", "Yuyang", "" ], [ "Li", "Ruichen", "" ], [ "Chardonnet", "Jean-Rémy", "" ], [ "Hui", "Pan", "" ] ]
new_dataset
0.999713
2303.13545
Manas Mehta
Manas Mehta, Nugzar Chkhaidze, and Yizhen Wang
Developing IncidentUI -- A Ride Comfort and Disengagement Evaluation Application for Autonomous Vehicles
Previously embargoed by Nvidia. Nvidia owns the rights
null
null
null
cs.HC cs.SE
http://creativecommons.org/licenses/by/4.0/
This report details the design, development, and implementation of IncidentUI, an Android tablet application designed to measure user-experienced ride comfort and record disengagement data for autonomous vehicles (AV) during test drives. The goal of our project was to develop an Android application to run on a peripheral tablet and communicate with the Drive Pegasus AGX, the AI Computing Platform for Nvidia's AV Level 2 Autonomy Solution Architecture [1], to detect AV disengagements and report ride comfort. We designed and developed an Android XML-based intuitive user interface for IncidentUI. The development of IncidentUI required a redesign of the system architecture by redeveloping the system communications protocol in Java and implementing the Protocol Buffers (Protobufs) in Java using the existing system Protobuf definitions. The final iteration of IncidentUI yielded the desired functionality while testing on an AV test drive. We also received positive feedback from Nvidia's AV Platform Team during our final IncidentUI demonstration.
[ { "version": "v1", "created": "Thu, 16 Mar 2023 21:30:58 GMT" } ]
2023-03-27T00:00:00
[ [ "Mehta", "Manas", "" ], [ "Chkhaidze", "Nugzar", "" ], [ "Wang", "Yizhen", "" ] ]
new_dataset
0.999612
2303.13548
Aparna Varde
Vishesh Kalvakurthi, Aparna S. Varde, John Jenq
Hey Dona! Can you help me with student course registration?
null
AAAI 2023 the 37th AAAI Conference on Artificial Intelligence (AI4EDU workshop)
null
null
cs.HC cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
In this paper, we present a demo of an intelligent personal agent called Hey Dona (or just Dona) with virtual voice assistance in student course registration. It is a deployed project in the theme of AI for education. In this digital age with a myriad of smart devices, users often delegate tasks to agents. While pointing and clicking supersedes the erstwhile command-typing, modern devices allow users to speak commands for agents to execute tasks, enhancing speed and convenience. In line with this progress, Dona is an intelligent agent catering to student needs by automated, voice-operated course registration, spanning a multitude of accents, entailing task planning optimization, with some language translation as needed. Dona accepts voice input by microphone (Bluetooth, wired microphone), converts human voice to computer understandable language, performs query processing as per user commands, connects with the Web to search for answers, models task dependencies, imbibes quality control, and conveys output by speaking to users as well as displaying text, thus enabling human-AI interaction by speech cum text. It is meant to work seamlessly on desktops, smartphones etc. and in indoor as well as outdoor settings. To the best of our knowledge, Dona is among the first of its kind as an intelligent personal agent for voice assistance in student course registration. Due to its ubiquitous access for educational needs, Dona directly impacts AI for education. It makes a broader impact on smart city characteristics of smart living and smart people due to its contributions to providing benefits for new ways of living and assisting 21st century education, respectively.
[ { "version": "v1", "created": "Tue, 21 Mar 2023 21:37:19 GMT" } ]
2023-03-27T00:00:00
[ [ "Kalvakurthi", "Vishesh", "" ], [ "Varde", "Aparna S.", "" ], [ "Jenq", "John", "" ] ]
new_dataset
0.982052
2303.13549
Aparna Varde
Levi Corallo and Aparna S. Varde
Optical Character Recognition and Transcription of Berber Signs from Images in a Low-Resource Language Amazigh
null
AAAI-2023 the 37th AAAI Conference on Artificial Intelligence (AI4EDU workshop)
null
null
cs.CV cs.AI cs.CL cs.LG eess.IV
http://creativecommons.org/licenses/by/4.0/
The Berber, or Amazigh language family is a low-resource North African vernacular language spoken by the indigenous Berber ethnic group. It has its own unique alphabet called Tifinagh used across Berber communities in Morocco, Algeria, and others. The Afroasiatic language Berber is spoken by 14 million people, yet lacks adequate representation in education, research, web applications etc. For instance, there is no option of translation to or from Amazigh / Berber on Google Translate, which hosts over 100 languages today. Consequently, we do not find specialized educational apps, L2 (2nd language learner) acquisition, automated language translation, and remote-access facilities enabled in Berber. Motivated by this background, we propose a supervised approach called DaToBS for Detection and Transcription of Berber Signs. The DaToBS approach entails the automatic recognition and transcription of Tifinagh characters from signs in photographs of natural environments. This is achieved by self-creating a corpus of 1862 pre-processed character images; curating the corpus with human-guided annotation; and feeding it into an OCR model via the deployment of CNN for deep learning based on computer vision models. We deploy computer vision modeling (rather than language models) because there are pictorial symbols in this alphabet, this deployment being a novel aspect of our work. The DaToBS experimentation and analyses yield over 92 percent accuracy in our research. To the best of our knowledge, ours is among the first few works in the automated transcription of Berber signs from roadside images with deep learning, yielding high accuracy. This can pave the way for developing pedagogical applications in the Berber language, thereby addressing an important goal of outreach to underrepresented communities via AI in education.
[ { "version": "v1", "created": "Tue, 21 Mar 2023 21:38:44 GMT" } ]
2023-03-27T00:00:00
[ [ "Corallo", "Levi", "" ], [ "Varde", "Aparna S.", "" ] ]
new_dataset
0.99949
2303.13550
Rob Eagle
Rob Eagle
Augmented reality as a Thirdspace: Simultaneous experience of the physical and virtual
Preprint of chapter published in Proceedings of the 3rd International and Interdisciplinary Conference on Images and Imagination, edited by D. Villa and F. Zuccoli, 2023, Springer Nature, reproduced with permission of Springer Nature
null
10.1007/978-3-031-25906-7_39
null
cs.HC cs.MM
http://creativecommons.org/licenses/by/4.0/
With the proliferation of devices that display augmented reality (AR), now is the time for scholars and practitioners to evaluate and engage critically with emerging applications of the medium. AR mediates the way users see their bodies, hear their environment and engage with places. Applied in various forms, including social media, e-commerce, gaming, enterprise and art, the medium facilitates a hybrid experience of physical and digital spaces. This article employs a model of real-and-imagined space from geographer Edward Soja to examine how the user of an AR app navigates the two intertwined spaces of physical and digital, experiencing what Soja calls a 'Third-space'. The article illustrates the potential for headset-based AR to engender such a Thirdspace through the author's practice-led research project, the installation Through the Wardrobe. This installation demonstrates how AR has the potential to shift the way that users view and interact with their world with artistic applications providing an opportunity to question assumptions of social norms, identity and uses of physical space.
[ { "version": "v1", "created": "Tue, 21 Mar 2023 22:46:22 GMT" } ]
2023-03-27T00:00:00
[ [ "Eagle", "Rob", "" ] ]
new_dataset
0.995864
2303.13604
Mohammad Pedramfar
Mohammad Pedramfar, Vaneet Aggarwal
Stochastic Submodular Bandits with Delayed Composite Anonymous Bandit Feedback
null
null
null
null
cs.LG cs.AI cs.DS
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper investigates the problem of combinatorial multiarmed bandits with stochastic submodular (in expectation) rewards and full-bandit delayed feedback, where the delayed feedback is assumed to be composite and anonymous. In other words, the delayed feedback is composed of components of rewards from past actions, with unknown division among the sub-components. Three models of delayed feedback: bounded adversarial, stochastic independent, and stochastic conditionally independent are studied, and regret bounds are derived for each of the delay models. Ignoring the problem dependent parameters, we show that regret bound for all the delay models is $\tilde{O}(T^{2/3} + T^{1/3} \nu)$ for time horizon $T$, where $\nu$ is a delay parameter defined differently in the three cases, thus demonstrating an additive term in regret with delay in all the three delay models. The considered algorithm is demonstrated to outperform other full-bandit approaches with delayed composite anonymous feedback.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 18:38:33 GMT" } ]
2023-03-27T00:00:00
[ [ "Pedramfar", "Mohammad", "" ], [ "Aggarwal", "Vaneet", "" ] ]
new_dataset
0.99759
2303.13675
Andrew Halterman
Andrew Halterman
Mordecai 3: A Neural Geoparser and Event Geocoder
6 pages, 1 figure, 4 tables
null
null
null
cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Mordecai3 is a new end-to-end text geoparser and event geolocation system. The system performs toponym resolution using a new neural ranking model to resolve a place name extracted from a document to its entry in the Geonames gazetteer. It also performs event geocoding, the process of linking events reported in text with the place names where they are reported to occur, using an off-the-shelf question-answering model. The toponym resolution model is trained on a diverse set of existing training data, along with several thousand newly annotated examples. The paper describes the model, its training process, and performance comparisons with existing geoparsers. The system is available as an open source Python library, Mordecai 3, and replaces an earlier geoparser, Mordecai v2, one of the most widely used text geoparsers (Halterman 2017).
[ { "version": "v1", "created": "Thu, 23 Mar 2023 21:10:04 GMT" } ]
2023-03-27T00:00:00
[ [ "Halterman", "Andrew", "" ] ]
new_dataset
0.994548
2303.13731
Yiran Li
Yiran Li, Junpeng Wang, Xin Dai, Liang Wang, Chin-Chia Michael Yeh, Yan Zheng, Wei Zhang, Kwan-Liu Ma
How Does Attention Work in Vision Transformers? A Visual Analytics Attempt
Accepted by PacificVis 2023 and selected to be published in TVCG
null
null
null
cs.LG cs.CV cs.HC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Vision transformer (ViT) expands the success of transformer models from sequential data to images. The model decomposes an image into many smaller patches and arranges them into a sequence. Multi-head self-attentions are then applied to the sequence to learn the attention between patches. Despite many successful interpretations of transformers on sequential data, little effort has been devoted to the interpretation of ViTs, and many questions remain unanswered. For example, among the numerous attention heads, which one is more important? How strong are individual patches attending to their spatial neighbors in different heads? What attention patterns have individual heads learned? In this work, we answer these questions through a visual analytics approach. Specifically, we first identify what heads are more important in ViTs by introducing multiple pruning-based metrics. Then, we profile the spatial distribution of attention strengths between patches inside individual heads, as well as the trend of attention strengths across attention layers. Third, using an autoencoder-based learning solution, we summarize all possible attention patterns that individual heads could learn. Examining the attention strengths and patterns of the important heads, we answer why they are important. Through concrete case studies with experienced deep learning experts on multiple ViTs, we validate the effectiveness of our solution that deepens the understanding of ViTs from head importance, head attention strength, and head attention pattern.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 01:02:59 GMT" } ]
2023-03-27T00:00:00
[ [ "Li", "Yiran", "" ], [ "Wang", "Junpeng", "" ], [ "Dai", "Xin", "" ], [ "Wang", "Liang", "" ], [ "Yeh", "Chin-Chia Michael", "" ], [ "Zheng", "Yan", "" ], [ "Zhang", "Wei", "" ], [ "Ma", "Kwan-Liu", "" ] ]
new_dataset
0.959314
2303.13733
Taeyoung Kim
Taeyoung Kim, Yunhee Jang, Chanjong Lee, Hyungjoon Koo, Hyoungshick Kim
SmartMark: Software Watermarking Scheme for Smart Contracts
This paper is accepted for publication in ICSE 2023
null
null
null
cs.SE
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Smart contracts are self-executing programs on a blockchain to ensure immutable and transparent agreements without the involvement of intermediaries. Despite the growing popularity of smart contracts for many blockchain platforms like Ethereum, smart contract developers cannot prevent copying their smart contracts from competitors due to the absence of technical means available. However, applying existing software watermarking techniques is challenging because of the unique properties of smart contracts, such as a code size constraint, non-free execution cost, and no support for dynamic allocation under a virtual machine environment. This paper introduces a novel software watermarking scheme, dubbed SmartMark, aiming to protect the piracy of smart contracts. SmartMark builds the control flow graph of a target contract runtime bytecode and locates a series of bytes randomly selected from a collection of opcodes to represent a watermark. We implement a full-fledged prototype for Ethereum, applying SmartMark to 27,824 unique smart contract bytecodes. Our empirical results demonstrate that SmartMark can effectively embed a watermark into smart contracts and verify its presence, meeting the requirements of credibility and imperceptibility while incurring a slight performance degradation. Furthermore, our security analysis shows that SmartMark is resilient against foreseeable watermarking corruption attacks; e.g., a large number of dummy opcodes are needed to disable a watermark effectively, resulting in producing illegitimate smart contract clones that are not economical.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 01:12:19 GMT" } ]
2023-03-27T00:00:00
[ [ "Kim", "Taeyoung", "" ], [ "Jang", "Yunhee", "" ], [ "Lee", "Chanjong", "" ], [ "Koo", "Hyungjoon", "" ], [ "Kim", "Hyoungshick", "" ] ]
new_dataset
0.996965
2303.13739
Rui Zhao
Yulin Luo, Rui Zhao, Xiaobao Wei, Jinwei Chen, Yijie Lu, Shenghao Xie, Tianyu Wang, Ruiqin Xiong, Ming Lu, Shanghang Zhang
MoWE: Mixture of Weather Experts for Multiple Adverse Weather Removal
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Currently, most adverse weather removal tasks are handled independently, such as deraining, desnowing, and dehazing. However, in autonomous driving scenarios, the type, intensity, and mixing degree of the weather are unknown, so the separated task setting cannot deal with these complex conditions well. Besides, the vision applications in autonomous driving often aim at high-level tasks, but existing weather removal methods neglect the connection between performance on perceptual tasks and signal fidelity. To this end, in upstream task, we propose a novel \textbf{Mixture of Weather Experts(MoWE)} Transformer framework to handle complex weather removal in a perception-aware fashion. We design a \textbf{Weather-aware Router} to make the experts targeted more relevant to weather types while without the need for weather type labels during inference. To handle diverse weather conditions, we propose \textbf{Multi-scale Experts} to fuse information among neighbor tokens. In downstream task, we propose a \textbf{Label-free Perception-aware Metric} to measure whether the outputs of image processing models are suitable for high level perception tasks without the demand for semantic labels. We collect a syntactic dataset \textbf{MAW-Sim} towards autonomous driving scenarios to benchmark the multiple weather removal performance of existing methods. Our MoWE achieves SOTA performance in upstream task on the proposed dataset and two public datasets, i.e. All-Weather and Rain/Fog-Cityscapes, and also have better perceptual results in downstream segmentation task compared to other methods. Our codes and datasets will be released after acceptance.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 01:46:25 GMT" } ]
2023-03-27T00:00:00
[ [ "Luo", "Yulin", "" ], [ "Zhao", "Rui", "" ], [ "Wei", "Xiaobao", "" ], [ "Chen", "Jinwei", "" ], [ "Lu", "Yijie", "" ], [ "Xie", "Shenghao", "" ], [ "Wang", "Tianyu", "" ], [ "Xiong", "Ruiqin", "" ], [ "Lu", "Ming", "" ], [ "Zhang", "Shanghang", "" ] ]
new_dataset
0.99887
2303.13740
Raul Rojas Prof.
Ra\'ul Rojas
The First Computer Program
8 pages, 4 tables
null
null
null
cs.GL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In 1837, the first computer program in history was sketched by the renowned mathematician and inventor Charles Babbage. It was a program for the Analytical Engine. The program consists of a sequence of arithmetical operations and the necessary variable addresses (memory locations) of the arguments and the result, displayed in tabular fashion, like a program trace. The program computes the solutions for a system of two linear equations in two unknowns.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 01:46:27 GMT" } ]
2023-03-27T00:00:00
[ [ "Rojas", "Raúl", "" ] ]
new_dataset
0.995769
2303.13743
Vishal Vinod
Vishal Vinod, Tanmay Shah, Dmitry Lagun
TEGLO: High Fidelity Canonical Texture Mapping from Single-View Images
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Recent work in Neural Fields (NFs) learn 3D representations from class-specific single view image collections. However, they are unable to reconstruct the input data preserving high-frequency details. Further, these methods do not disentangle appearance from geometry and hence are not suitable for tasks such as texture transfer and editing. In this work, we propose TEGLO (Textured EG3D-GLO) for learning 3D representations from single view in-the-wild image collections for a given class of objects. We accomplish this by training a conditional Neural Radiance Field (NeRF) without any explicit 3D supervision. We equip our method with editing capabilities by creating a dense correspondence mapping to a 2D canonical space. We demonstrate that such mapping enables texture transfer and texture editing without requiring meshes with shared topology. Our key insight is that by mapping the input image pixels onto the texture space we can achieve near perfect reconstruction (>= 74 dB PSNR at 1024^2 resolution). Our formulation allows for high quality 3D consistent novel view synthesis with high-frequency details at megapixel image resolution.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 01:52:03 GMT" } ]
2023-03-27T00:00:00
[ [ "Vinod", "Vishal", "" ], [ "Shah", "Tanmay", "" ], [ "Lagun", "Dmitry", "" ] ]
new_dataset
0.991997
2303.13806
Xusheng Zhu
Xusheng Zhu, Wen Chen, Zhendong Li, Qingqing Wu, and Jun Li
Quadrature Spatial Scattering Modulation for mmWave Transmission
null
null
null
null
cs.IT eess.SP math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this letter, we investigate a novel quadrature spatial scattering modulation (QSSM) transmission technique based on millimeter wave (mmWave) systems, in which the transmitter generates two orthogonal beams targeting candidate scatterers in the channel to carry the real and imaginary parts of the conventional signal, respectively. Meanwhile, the maximum likelihood (ML) detector is adopted at the receiver to recover the received beams and signals. Based on the ML detector, we derive the closed-form average bit error probability (ABEP) expression of the QSSM scheme. Furthermore, we evaluate the asymptotic ABEP expression of the proposed scheme. Monte Carlo simulations verify the exactness and tightness of the derivation results. It is shown that the ABEP performance of QSSM is better than that of traditional spatial scattering modulation.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 05:00:38 GMT" } ]
2023-03-27T00:00:00
[ [ "Zhu", "Xusheng", "" ], [ "Chen", "Wen", "" ], [ "Li", "Zhendong", "" ], [ "Wu", "Qingqing", "" ], [ "Li", "Jun", "" ] ]
new_dataset
0.9969
2303.13807
Juncheng Li
Hansheng Guo, Juncheng Li, Guangwei Gao, Zhi Li, Tieyong Zeng
PFT-SSR: Parallax Fusion Transformer for Stereo Image Super-Resolution
5 pages, 3 figures
ICASSP 2023
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Stereo image super-resolution aims to boost the performance of image super-resolution by exploiting the supplementary information provided by binocular systems. Although previous methods have achieved promising results, they did not fully utilize the information of cross-view and intra-view. To further unleash the potential of binocular images, in this letter, we propose a novel Transformerbased parallax fusion module called Parallax Fusion Transformer (PFT). PFT employs a Cross-view Fusion Transformer (CVFT) to utilize cross-view information and an Intra-view Refinement Transformer (IVRT) for intra-view feature refinement. Meanwhile, we adopted the Swin Transformer as the backbone for feature extraction and SR reconstruction to form a pure Transformer architecture called PFT-SSR. Extensive experiments and ablation studies show that PFT-SSR achieves competitive results and outperforms most SOTA methods. Source code is available at https://github.com/MIVRC/PFT-PyTorch.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 05:04:52 GMT" } ]
2023-03-27T00:00:00
[ [ "Guo", "Hansheng", "" ], [ "Li", "Juncheng", "" ], [ "Gao", "Guangwei", "" ], [ "Li", "Zhi", "" ], [ "Zeng", "Tieyong", "" ] ]
new_dataset
0.994086
2303.13825
Zhiyang Guo
Zhiyang Guo, Wengang Zhou, Min Wang, Li Li, Houqiang Li
HandNeRF: Neural Radiance Fields for Animatable Interacting Hands
CVPR 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We propose a novel framework to reconstruct accurate appearance and geometry with neural radiance fields (NeRF) for interacting hands, enabling the rendering of photo-realistic images and videos for gesture animation from arbitrary views. Given multi-view images of a single hand or interacting hands, an off-the-shelf skeleton estimator is first employed to parameterize the hand poses. Then we design a pose-driven deformation field to establish correspondence from those different poses to a shared canonical space, where a pose-disentangled NeRF for one hand is optimized. Such unified modeling efficiently complements the geometry and texture cues in rarely-observed areas for both hands. Meanwhile, we further leverage the pose priors to generate pseudo depth maps as guidance for occlusion-aware density learning. Moreover, a neural feature distillation method is proposed to achieve cross-domain alignment for color optimization. We conduct extensive experiments to verify the merits of our proposed HandNeRF and report a series of state-of-the-art results both qualitatively and quantitatively on the large-scale InterHand2.6M dataset.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 06:19:19 GMT" } ]
2023-03-27T00:00:00
[ [ "Guo", "Zhiyang", "" ], [ "Zhou", "Wengang", "" ], [ "Wang", "Min", "" ], [ "Li", "Li", "" ], [ "Li", "Houqiang", "" ] ]
new_dataset
0.980831
2303.13839
Jiefeng Ma
Jiefeng Ma, Jun Du, Pengfei Hu, Zhenrong Zhang, Jianshu Zhang, Huihui Zhu, Cong Liu
HRDoc: Dataset and Baseline Method Toward Hierarchical Reconstruction of Document Structures
8 pages, 6 figures. Accepted by AAAI-2023
null
null
null
cs.CL cs.CV
http://creativecommons.org/licenses/by-nc-sa/4.0/
The problem of document structure reconstruction refers to converting digital or scanned documents into corresponding semantic structures. Most existing works mainly focus on splitting the boundary of each element in a single document page, neglecting the reconstruction of semantic structure in multi-page documents. This paper introduces hierarchical reconstruction of document structures as a novel task suitable for NLP and CV fields. To better evaluate the system performance on the new task, we built a large-scale dataset named HRDoc, which consists of 2,500 multi-page documents with nearly 2 million semantic units. Every document in HRDoc has line-level annotations including categories and relations obtained from rule-based extractors and human annotators. Moreover, we proposed an encoder-decoder-based hierarchical document structure parsing system (DSPS) to tackle this problem. By adopting a multi-modal bidirectional encoder and a structure-aware GRU decoder with soft-mask operation, the DSPS model surpass the baseline method by a large margin. All scripts and datasets will be made publicly available at https://github.com/jfma-USTC/HRDoc.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 07:23:56 GMT" } ]
2023-03-27T00:00:00
[ [ "Ma", "Jiefeng", "" ], [ "Du", "Jun", "" ], [ "Hu", "Pengfei", "" ], [ "Zhang", "Zhenrong", "" ], [ "Zhang", "Jianshu", "" ], [ "Zhu", "Huihui", "" ], [ "Liu", "Cong", "" ] ]
new_dataset
0.99972
2303.13859
Chunyi Li
Xinhui Huang, Chunyi Li, Abdelhak Bentaleb, Roger Zimmermann, Guangtao Zhai
XGC-VQA: A unified video quality assessment model for User, Professionally, and Occupationally-Generated Content
6 pages, 4 figures
null
null
null
cs.MM eess.IV
http://creativecommons.org/licenses/by/4.0/
With the rapid growth of Internet video data amounts and types, a unified Video Quality Assessment (VQA) is needed to inspire video communication with perceptual quality. To meet the real-time and universal requirements in providing such inspiration, this study proposes a VQA model from a classification of User Generated Content (UGC), Professionally Generated Content (PGC), and Occupationally Generated Content (OGC). In the time domain, this study utilizes non-uniform sampling, as each content type has varying temporal importance based on its perceptual quality. In the spatial domain, centralized downsampling is performed before the VQA process by utilizing a patch splicing/sampling mechanism to lower complexity for real-time assessment. The experimental results demonstrate that the proposed method achieves a median correlation of $0.7$ while limiting the computation time below 5s for three content types, which ensures that the communication experience of UGC, PGC, and OGC can be optimized altogether.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 08:47:02 GMT" } ]
2023-03-27T00:00:00
[ [ "Huang", "Xinhui", "" ], [ "Li", "Chunyi", "" ], [ "Bentaleb", "Abdelhak", "" ], [ "Zimmermann", "Roger", "" ], [ "Zhai", "Guangtao", "" ] ]
new_dataset
0.978468
2303.13868
Xingxing Wei
Wei Xingxing and Yu Jie and Huang Yao
Physically Adversarial Infrared Patches with Learnable Shapes and Locations
accepted by CVPR2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Owing to the extensive application of infrared object detectors in the safety-critical tasks, it is necessary to evaluate their robustness against adversarial examples in the real world. However, current few physical infrared attacks are complicated to implement in practical application because of their complex transformation from digital world to physical world. To address this issue, in this paper, we propose a physically feasible infrared attack method called "adversarial infrared patches". Considering the imaging mechanism of infrared cameras by capturing objects' thermal radiation, adversarial infrared patches conduct attacks by attaching a patch of thermal insulation materials on the target object to manipulate its thermal distribution. To enhance adversarial attacks, we present a novel aggregation regularization to guide the simultaneous learning for the patch' shape and location on the target object. Thus, a simple gradient-based optimization can be adapted to solve for them. We verify adversarial infrared patches in different object detection tasks with various object detectors. Experimental results show that our method achieves more than 90\% Attack Success Rate (ASR) versus the pedestrian detector and vehicle detector in the physical environment, where the objects are captured in different angles, distances, postures, and scenes. More importantly, adversarial infrared patch is easy to implement, and it only needs 0.5 hours to be constructed in the physical world, which verifies its effectiveness and efficiency.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 09:11:36 GMT" } ]
2023-03-27T00:00:00
[ [ "Xingxing", "Wei", "" ], [ "Jie", "Yu", "" ], [ "Yao", "Huang", "" ] ]
new_dataset
0.998528
2303.13885
Junsong Chen
Haojie Zhao and Junsong Chen and Lijun Wang and Huchuan Lu
ARKitTrack: A New Diverse Dataset for Tracking Using Mobile RGB-D Data
Accepted by CVPR2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Compared with traditional RGB-only visual tracking, few datasets have been constructed for RGB-D tracking. In this paper, we propose ARKitTrack, a new RGB-D tracking dataset for both static and dynamic scenes captured by consumer-grade LiDAR scanners equipped on Apple's iPhone and iPad. ARKitTrack contains 300 RGB-D sequences, 455 targets, and 229.7K video frames in total. Along with the bounding box annotations and frame-level attributes, we also annotate this dataset with 123.9K pixel-level target masks. Besides, the camera intrinsic and camera pose of each frame are provided for future developments. To demonstrate the potential usefulness of this dataset, we further present a unified baseline for both box-level and pixel-level tracking, which integrates RGB features with bird's-eye-view representations to better explore cross-modality 3D geometry. In-depth empirical analysis has verified that the ARKitTrack dataset can significantly facilitate RGB-D tracking and that the proposed baseline method compares favorably against the state of the arts. The code and dataset is available at https://arkittrack.github.io.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 09:51:13 GMT" } ]
2023-03-27T00:00:00
[ [ "Zhao", "Haojie", "" ], [ "Chen", "Junsong", "" ], [ "Wang", "Lijun", "" ], [ "Lu", "Huchuan", "" ] ]
new_dataset
0.999842
2303.13903
Timo H\"ackel
Timo H\"ackel, Philipp Meyer, Mehmet Mueller, Jan Schmitt-Solbrig, Franz Korf, Thomas C. Schmidt
Dynamic Service-Orientation for Software-Defined In-Vehicle Networks
null
null
null
null
cs.NI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Modern In-Vehicle Networks (IVNs) are composed of a large number of devices and services linked via an Ethernet-based time-sensitive network. Communication in future IVNs will become more dynamic as services can be updated, added, or removed during runtime. This requires a flexible and adaptable IVN, for which Software-Defined Networking (SDN) is a promising candidate. In this paper, we show how SDN can be used to support a dynamic, service-oriented network architecture. We demonstrate our concept using the SOME/IP protocol, which is the most widely deployed implementation of automotive service-oriented architectures. In a simulation study, we evaluate the performance of SOME/IP-adaptive SDN control compared to standard Ethernet switching and non-optimized SDN. Our results show an expected overhead introduced by the central SDN controller, which is, however, reduced by up to 50% compared to SOME/IP-unaware SDN.For a large number of services, the setup time is in the order of milliseconds, which matches standard Ethernet switching. A SOME/IP-aware SDN controller can optimize the service discovery to improve adaptability, robustness, security, and Quality-of-Service of the IVN while remaining transparent to existing SOME/IP implementations.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 10:32:10 GMT" } ]
2023-03-27T00:00:00
[ [ "Häckel", "Timo", "" ], [ "Meyer", "Philipp", "" ], [ "Mueller", "Mehmet", "" ], [ "Schmitt-Solbrig", "Jan", "" ], [ "Korf", "Franz", "" ], [ "Schmidt", "Thomas C.", "" ] ]
new_dataset
0.988367
2303.13913
Han Xue
Han Xue, Wenqiang Xu, Jieyi Zhang, Tutian Tang, Yutong Li, Wenxin Du, Ruolin Ye, Cewu Lu
GarmentTracking: Category-Level Garment Pose Tracking
null
null
null
null
cs.CV cs.RO
http://creativecommons.org/licenses/by/4.0/
Garments are important to humans. A visual system that can estimate and track the complete garment pose can be useful for many downstream tasks and real-world applications. In this work, we present a complete package to address the category-level garment pose tracking task: (1) A recording system VR-Garment, with which users can manipulate virtual garment models in simulation through a VR interface. (2) A large-scale dataset VR-Folding, with complex garment pose configurations in manipulation like flattening and folding. (3) An end-to-end online tracking framework GarmentTracking, which predicts complete garment pose both in canonical space and task space given a point cloud sequence. Extensive experiments demonstrate that the proposed GarmentTracking achieves great performance even when the garment has large non-rigid deformation. It outperforms the baseline approach on both speed and accuracy. We hope our proposed solution can serve as a platform for future research. Codes and datasets are available in https://garment-tracking.robotflow.ai.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 10:59:17 GMT" } ]
2023-03-27T00:00:00
[ [ "Xue", "Han", "" ], [ "Xu", "Wenqiang", "" ], [ "Zhang", "Jieyi", "" ], [ "Tang", "Tutian", "" ], [ "Li", "Yutong", "" ], [ "Du", "Wenxin", "" ], [ "Ye", "Ruolin", "" ], [ "Lu", "Cewu", "" ] ]
new_dataset
0.999702
2303.13931
Yousri Kessentini
Marwa Dhiaf, Ahmed Cheikh Rouhou, Yousri Kessentini, Sinda Ben Salem
MSdocTr-Lite: A Lite Transformer for Full Page Multi-script Handwriting Recognition
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
The Transformer has quickly become the dominant architecture for various pattern recognition tasks due to its capacity for long-range representation. However, transformers are data-hungry models and need large datasets for training. In Handwritten Text Recognition (HTR), collecting a massive amount of labeled data is a complicated and expensive task. In this paper, we propose a lite transformer architecture for full-page multi-script handwriting recognition. The proposed model comes with three advantages: First, to solve the common problem of data scarcity, we propose a lite transformer model that can be trained on a reasonable amount of data, which is the case of most HTR public datasets, without the need for external data. Second, it can learn the reading order at page-level thanks to a curriculum learning strategy, allowing it to avoid line segmentation errors, exploit a larger context and reduce the need for costly segmentation annotations. Third, it can be easily adapted to other scripts by applying a simple transfer-learning process using only page-level labeled images. Extensive experiments on different datasets with different scripts (French, English, Spanish, and Arabic) show the effectiveness of the proposed model.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 11:40:50 GMT" } ]
2023-03-27T00:00:00
[ [ "Dhiaf", "Marwa", "" ], [ "Rouhou", "Ahmed Cheikh", "" ], [ "Kessentini", "Yousri", "" ], [ "Salem", "Sinda Ben", "" ] ]
new_dataset
0.997729
2303.13965
Rashmi Kushwaha
Rashmi Kushwaha, Shreyas Kulkarni, Yatindra Nath Singh
Generalized Distance Metric for Different DHT Routing Algorithms in Peer-to-Peer Networks
7 pages, 4 figures, 13 Tables
null
null
null
cs.NI
http://creativecommons.org/licenses/by-nc-nd/4.0/
We present a generalized distance metric that can be used to identify routing table entries and implement routing strategies to reach the root node for a given key, in DHT (Distributed Hash Table) networks such as Chord, Kademlia, Tapestry, and Pastry. The generalization shows that all the four DHT algorithms are in fact, the same algorithm but with different parameters in distance representation. This paper also proposes that nodes can have routing tables of varying sizes based on their memory capabilities. But Each node must have at least two entries, one for the node closest from it, and the other for the node from whom it is closest, regardless of memory capacity. With this condition, messages will still reach the correct root nodes. We also further observe that in any network, if the distance metric of the DHT is same at all the nodes, then the root node for a key will also be the same, irrespective of the size of the routing table at different nodes.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 12:38:00 GMT" } ]
2023-03-27T00:00:00
[ [ "Kushwaha", "Rashmi", "" ], [ "Kulkarni", "Shreyas", "" ], [ "Singh", "Yatindra Nath", "" ] ]
new_dataset
0.984793
2303.14087
Xiaohao Sun
Xiaohao Sun, Hanxiao Jiang, Manolis Savva, Angel Xuan Chang
OPDMulti: Openable Part Detection for Multiple Objects
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Openable part detection is the task of detecting the openable parts of an object in a single-view image, and predicting corresponding motion parameters. Prior work investigated the unrealistic setting where all input images only contain a single openable object. We generalize this task to scenes with multiple objects each potentially possessing openable parts, and create a corresponding dataset based on real-world scenes. We then address this more challenging scenario with OPDFormer: a part-aware transformer architecture. Our experiments show that the OPDFormer architecture significantly outperforms prior work. The more realistic multiple-object scenarios we investigated remain challenging for all methods, indicating opportunities for future work.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 15:52:20 GMT" } ]
2023-03-27T00:00:00
[ [ "Sun", "Xiaohao", "" ], [ "Jiang", "Hanxiao", "" ], [ "Savva", "Manolis", "" ], [ "Chang", "Angel Xuan", "" ] ]
new_dataset
0.990028
2303.14126
Jordan J. Bird
Jordan J. Bird, Ahmad Lotfi
CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images
null
null
null
null
cs.CV cs.AI cs.LG
http://creativecommons.org/licenses/by/4.0/
Recent technological advances in synthetic data have enabled the generation of images with such high quality that human beings cannot tell the difference between real-life photographs and Artificial Intelligence (AI) generated images. Given the critical necessity of data reliability and authentication, this article proposes to enhance our ability to recognise AI-generated images through computer vision. Initially, a synthetic dataset is generated that mirrors the ten classes of the already available CIFAR-10 dataset with latent diffusion which provides a contrasting set of images for comparison to real photographs. The model is capable of generating complex visual attributes, such as photorealistic reflections in water. The two sets of data present as a binary classification problem with regard to whether the photograph is real or generated by AI. This study then proposes the use of a Convolutional Neural Network (CNN) to classify the images into two categories; Real or Fake. Following hyperparameter tuning and the training of 36 individual network topologies, the optimal approach could correctly classify the images with 92.98% accuracy. Finally, this study implements explainable AI via Gradient Class Activation Mapping to explore which features within the images are useful for classification. Interpretation reveals interesting concepts within the image, in particular, noting that the actual entity itself does not hold useful information for classification; instead, the model focuses on small visual imperfections in the background of the images. The complete dataset engineered for this study, referred to as the CIFAKE dataset, is made publicly available to the research community for future work.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 16:33:06 GMT" } ]
2023-03-27T00:00:00
[ [ "Bird", "Jordan J.", "" ], [ "Lotfi", "Ahmad", "" ] ]
new_dataset
0.999624
2303.14139
Huiguang He
Yizhuo Lu, Changde Du, Dianpeng Wang and Huiguang He
MindDiffuser: Controlled Image Reconstruction from Human Brain Activity with Semantic and Structural Diffusion
null
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Reconstructing visual stimuli from measured functional magnetic resonance imaging (fMRI) has been a meaningful and challenging task. Previous studies have successfully achieved reconstructions with structures similar to the original images, such as the outlines and size of some natural images. However, these reconstructions lack explicit semantic information and are difficult to discern. In recent years, many studies have utilized multi-modal pre-trained models with stronger generative capabilities to reconstruct images that are semantically similar to the original ones. However, these images have uncontrollable structural information such as position and orientation. To address both of the aforementioned issues simultaneously, we propose a two-stage image reconstruction model called MindDiffuser, utilizing Stable Diffusion. In Stage 1, the VQ-VAE latent representations and the CLIP text embeddings decoded from fMRI are put into the image-to-image process of Stable Diffusion, which yields a preliminary image that contains semantic and structural information. In Stage 2, we utilize the low-level CLIP visual features decoded from fMRI as supervisory information, and continually adjust the two features in Stage 1 through backpropagation to align the structural information. The results of both qualitative and quantitative analyses demonstrate that our proposed model has surpassed the current state-of-the-art models in terms of reconstruction results on Natural Scenes Dataset (NSD). Furthermore, the results of ablation experiments indicate that each component of our model is effective for image reconstruction.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 16:41:42 GMT" } ]
2023-03-27T00:00:00
[ [ "Lu", "Yizhuo", "" ], [ "Du", "Changde", "" ], [ "Wang", "Dianpeng", "" ], [ "He", "Huiguang", "" ] ]
new_dataset
0.975397
2303.14143
Evan King
Evan King, Haoxiang Yu, Sangsu Lee, and Christine Julien
"Get ready for a party": Exploring smarter smart spaces with help from large language models
7 pages, 4 figures
null
null
null
cs.HC cs.AI
http://creativecommons.org/licenses/by-nc-sa/4.0/
The right response to someone who says "get ready for a party" is deeply influenced by meaning and context. For a smart home assistant (e.g., Google Home), the ideal response might be to survey the available devices in the home and change their state to create a festive atmosphere. Current practical systems cannot service such requests since they require the ability to (1) infer meaning behind an abstract statement and (2) map that inference to a concrete course of action appropriate for the context (e.g., changing the settings of specific devices). In this paper, we leverage the observation that recent task-agnostic large language models (LLMs) like GPT-3 embody a vast amount of cross-domain, sometimes unpredictable contextual knowledge that existing rule-based home assistant systems lack, which can make them powerful tools for inferring user intent and generating appropriate context-dependent responses during smart home interactions. We first explore the feasibility of a system that places an LLM at the center of command inference and action planning, showing that LLMs have the capacity to infer intent behind vague, context-dependent commands like "get ready for a party" and respond with concrete, machine-parseable instructions that can be used to control smart devices. We furthermore demonstrate a proof-of-concept implementation that puts an LLM in control of real devices, showing its ability to infer intent and change device state appropriately with no fine-tuning or task-specific training. Our work hints at the promise of LLM-driven systems for context-awareness in smart environments, motivating future research in this area.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 16:51:08 GMT" } ]
2023-03-27T00:00:00
[ [ "King", "Evan", "" ], [ "Yu", "Haoxiang", "" ], [ "Lee", "Sangsu", "" ], [ "Julien", "Christine", "" ] ]
new_dataset
0.970895
2303.14174
Camilo Sanchez
Camilo Sanchez and Felix A. Epp
Experiential Futures In-the-wild to Inform Policy Design
null
null
null
null
cs.HC
http://creativecommons.org/licenses/by-nc-sa/4.0/
As technological innovation continues to shape our world at an accelerating pace, policy makers struggle to keep up with the unintended consequences of these new technologies. To address this policy-novelty gap, Responsible Research Innovation (RRI) has been proposed as a way to drive science and technology innovation towards socially desirable goals. This work suggests a more active HCI's position in the materialisation of pluralistic future visions and emphasizes the engagement between policy design and HCI for more agile and responsive evaluation environments. It calls for both fields to engage in questioning which and how futures are constructed, who they are benefiting, and how the findings of these interventions are interpreted towards other futures.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 17:37:25 GMT" } ]
2023-03-27T00:00:00
[ [ "Sanchez", "Camilo", "" ], [ "Epp", "Felix A.", "" ] ]
new_dataset
0.981201
2303.14190
Junxuan Li
Ziang Cheng, Junxuan Li, Hongdong Li
WildLight: In-the-wild Inverse Rendering with a Flashlight
Accepted to CVPR23. Website: https://junxuan-li.github.io/wildlight-website/
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper proposes a practical photometric solution for the challenging problem of in-the-wild inverse rendering under unknown ambient lighting. Our system recovers scene geometry and reflectance using only multi-view images captured by a smartphone. The key idea is to exploit smartphone's built-in flashlight as a minimally controlled light source, and decompose image intensities into two photometric components -- a static appearance corresponds to ambient flux, plus a dynamic reflection induced by the moving flashlight. Our method does not require flash/non-flash images to be captured in pairs. Building on the success of neural light fields, we use an off-the-shelf method to capture the ambient reflections, while the flashlight component enables physically accurate photometric constraints to decouple reflectance and illumination. Compared to existing inverse rendering methods, our setup is applicable to non-darkroom environments yet sidesteps the inherent difficulties of explicit solving ambient reflections. We demonstrate by extensive experiments that our method is easy to implement, casual to set up, and consistently outperforms existing in-the-wild inverse rendering techniques. Finally, our neural reconstruction can be easily exported to PBR textured triangle mesh ready for industrial renderers.
[ { "version": "v1", "created": "Fri, 24 Mar 2023 17:59:56 GMT" } ]
2023-03-27T00:00:00
[ [ "Cheng", "Ziang", "" ], [ "Li", "Junxuan", "" ], [ "Li", "Hongdong", "" ] ]
new_dataset
0.996081
1908.04531
Leon Derczynski
Gudbjartur Ingi Sigurbergsson, Leon Derczynski
Offensive Language and Hate Speech Detection for Danish
Proceedings of the Twelfth Language Resources and Evaluation Conference
null
null
null
cs.CL
http://creativecommons.org/licenses/by/4.0/
The presence of offensive language on social media platforms and the implications this poses is becoming a major concern in modern society. Given the enormous amount of content created every day, automatic methods are required to detect and deal with this type of content. Until now, most of the research has focused on solving the problem for the English language, while the problem is multilingual. We construct a Danish dataset containing user-generated comments from \textit{Reddit} and \textit{Facebook}. It contains user generated comments from various social media platforms, and to our knowledge, it is the first of its kind. Our dataset is annotated to capture various types and target of offensive language. We develop four automatic classification systems, each designed to work for both the English and the Danish language. In the detection of offensive language in English, the best performing system achieves a macro averaged F1-score of $0.74$, and the best performing system for Danish achieves a macro averaged F1-score of $0.70$. In the detection of whether or not an offensive post is targeted, the best performing system for English achieves a macro averaged F1-score of $0.62$, while the best performing system for Danish achieves a macro averaged F1-score of $0.73$. Finally, in the detection of the target type in a targeted offensive post, the best performing system for English achieves a macro averaged F1-score of $0.56$, and the best performing system for Danish achieves a macro averaged F1-score of $0.63$. Our work for both the English and the Danish language captures the type and targets of offensive language, and present automatic methods for detecting different kinds of offensive language such as hate speech and cyberbullying.
[ { "version": "v1", "created": "Tue, 13 Aug 2019 08:29:48 GMT" }, { "version": "v2", "created": "Thu, 23 Mar 2023 04:24:09 GMT" } ]
2023-03-24T00:00:00
[ [ "Sigurbergsson", "Gudbjartur Ingi", "" ], [ "Derczynski", "Leon", "" ] ]
new_dataset
0.99986
2108.09184
Adam Michael Roberts
Joe Gildea, Adrian Korban, Adam Michael Roberts, Alexander Tylyshchak
New binary self-dual codes of lengths 56, 62, 78, 92 and 94 from a bordered construction
corrected typos; other minor corrections. arXiv admin note: substantial text overlap with arXiv:2102.10354, arXiv:2106.12355, arXiv:2102.12326
null
10.1016/j.disc.2023.113425
null
cs.IT math.CO math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we present a new bordered construction for self-dual codes which employs $\lambda$-circulant matrices. We give the necessary conditions for our construction to produce self-dual codes over a finite commutative Frobenius ring of characteristic 2. Moreover, using our bordered construction together with the well-known building-up and neighbour methods, we construct many binary self-dual codes of lengths 56, 62, 78, 92 and 94 with parameters in their weight enumerators that were not known in the literature before.
[ { "version": "v1", "created": "Fri, 20 Aug 2021 14:00:58 GMT" }, { "version": "v2", "created": "Thu, 3 Feb 2022 15:33:46 GMT" } ]
2023-03-24T00:00:00
[ [ "Gildea", "Joe", "" ], [ "Korban", "Adrian", "" ], [ "Roberts", "Adam Michael", "" ], [ "Tylyshchak", "Alexander", "" ] ]
new_dataset
0.999358
2204.08696
Guangwei Gao
Guangwei Gao, Zixiang Xu, Juncheng Li, Jian Yang, Tieyong Zeng and Guo-Jun Qi
CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
IEEE Transactions on Image Processing, 12 figures, 9 tables
null
10.1109/TIP.2023.3261747
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recently, deep convolution neural networks (CNNs) steered face super-resolution methods have achieved great progress in restoring degraded facial details by jointly training with facial priors. However, these methods have some obvious limitations. On the one hand, multi-task joint learning requires additional marking on the dataset, and the introduced prior network will significantly increase the computational cost of the model. On the other hand, the limited receptive field of CNN will reduce the fidelity and naturalness of the reconstructed facial images, resulting in suboptimal reconstructed images. In this work, we propose an efficient CNN-Transformer Cooperation Network (CTCNet) for face super-resolution tasks, which uses the multi-scale connected encoder-decoder architecture as the backbone. Specifically, we first devise a novel Local-Global Feature Cooperation Module (LGCM), which is composed of a Facial Structure Attention Unit (FSAU) and a Transformer block, to promote the consistency of local facial detail and global facial structure restoration simultaneously. Then, we design an efficient Feature Refinement Module (FRM) to enhance the encoded features. Finally, to further improve the restoration of fine facial details, we present a Multi-scale Feature Fusion Unit (MFFU) to adaptively fuse the features from different stages in the encoder procedure. Extensive evaluations on various datasets have assessed that the proposed CTCNet can outperform other state-of-the-art methods significantly. Source code will be available at https://github.com/IVIPLab/CTCNet.
[ { "version": "v1", "created": "Tue, 19 Apr 2022 06:38:29 GMT" }, { "version": "v2", "created": "Mon, 30 Jan 2023 01:51:04 GMT" }, { "version": "v3", "created": "Thu, 23 Mar 2023 09:44:22 GMT" } ]
2023-03-24T00:00:00
[ [ "Gao", "Guangwei", "" ], [ "Xu", "Zixiang", "" ], [ "Li", "Juncheng", "" ], [ "Yang", "Jian", "" ], [ "Zeng", "Tieyong", "" ], [ "Qi", "Guo-Jun", "" ] ]
new_dataset
0.990776
2208.10295
Wouter Jansen
Wouter Jansen, Nico Huebel, Jan Steckel
Physical LiDAR Simulation in Real-Time Engine
IEEE Sensors 2022 Conference, Dallas, TX, USA
null
10.1109/SENSORS52175.2022.9967197
null
cs.RO eess.SP
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Designing and validating sensor applications and algorithms in simulation is an important step in the modern development process. Furthermore, modern open-source multi-sensor simulation frameworks are moving towards the usage of video-game engines such as the Unreal Engine. Simulation of a sensor such as a LiDAR can prove to be difficult in such real-time software. In this paper we present a GPU-accelerated simulation of LiDAR based on its physical properties and interaction with the environment. We provide a generation of the depth and intensity data based on the properties of the sensor as well as the surface material and incidence angle at which the light beams hit the surface. It is validated against a real LiDAR sensor and shown to be accurate and precise although highly depended on the spectral data used for the material properties.
[ { "version": "v1", "created": "Mon, 22 Aug 2022 13:23:17 GMT" }, { "version": "v2", "created": "Tue, 23 Aug 2022 08:43:01 GMT" }, { "version": "v3", "created": "Mon, 19 Dec 2022 09:30:18 GMT" } ]
2023-03-24T00:00:00
[ [ "Jansen", "Wouter", "" ], [ "Huebel", "Nico", "" ], [ "Steckel", "Jan", "" ] ]
new_dataset
0.99792
2211.03456
Xin Jin
Xin Jin, Longhai Wu, Jie Chen, Youxin Chen, Jayoon Koo, Cheul-hee Hahm
A Unified Pyramid Recurrent Network for Video Frame Interpolation
Accepted by CVPR 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Flow-guided synthesis provides a common framework for frame interpolation, where optical flow is estimated to guide the synthesis of intermediate frames between consecutive inputs. In this paper, we present UPR-Net, a novel Unified Pyramid Recurrent Network for frame interpolation. Cast in a flexible pyramid framework, UPR-Net exploits lightweight recurrent modules for both bi-directional flow estimation and intermediate frame synthesis. At each pyramid level, it leverages estimated bi-directional flow to generate forward-warped representations for frame synthesis; across pyramid levels, it enables iterative refinement for both optical flow and intermediate frame. In particular, we show that our iterative synthesis strategy can significantly improve the robustness of frame interpolation on large motion cases. Despite being extremely lightweight (1.7M parameters), our base version of UPR-Net achieves excellent performance on a large range of benchmarks. Code and trained models of our UPR-Net series are available at: https://github.com/srcn-ivl/UPR-Net.
[ { "version": "v1", "created": "Mon, 7 Nov 2022 11:12:31 GMT" }, { "version": "v2", "created": "Thu, 23 Mar 2023 04:14:45 GMT" } ]
2023-03-24T00:00:00
[ [ "Jin", "Xin", "" ], [ "Wu", "Longhai", "" ], [ "Chen", "Jie", "" ], [ "Chen", "Youxin", "" ], [ "Koo", "Jayoon", "" ], [ "Hahm", "Cheul-hee", "" ] ]
new_dataset
0.985499
2211.03704
Patrice Ossona de Mendez
J. Nesetril and P. Ossona de Mendez and S. Siebertz
Modulo-Counting First-Order Logic on Bounded Expansion Classes
submitted to CSGT2022 special issue
null
null
null
cs.LO math.CO
http://creativecommons.org/licenses/by-nc-sa/4.0/
We prove that, on bounded expansion classes, every first-order formula with modulo counting is equivalent, in a linear-time computable monadic expansion, to an existential first-order formula. As a consequence, we derive, on bounded expansion classes, that first-order transductions with modulo counting have the same encoding power as existential first-order transductions. Also, modulo-counting first-order model checking and computation of the size of sets definable in modulo-counting first-order logic can be achieved in linear time on bounded expansion classes. As an application, we prove that a class has structurally bounded expansion if and only if it is a class of bounded depth vertex-minors of graphs in a bounded expansion class. We also show how our results can be used to implement fast matrix calculus on bounded expansion matrices over a finite field.
[ { "version": "v1", "created": "Mon, 7 Nov 2022 17:20:37 GMT" }, { "version": "v2", "created": "Thu, 23 Mar 2023 09:31:58 GMT" } ]
2023-03-24T00:00:00
[ [ "Nesetril", "J.", "" ], [ "de Mendez", "P. Ossona", "" ], [ "Siebertz", "S.", "" ] ]
new_dataset
0.998469
2211.11270
Cheng Guo
Cheng Guo and Xiuhua Jiang
LHDR: HDR Reconstruction for Legacy Content using a Lightweight DNN
Accepted in ACCV2022
null
10.1007/978-3-031-26313-2_19
null
cs.CV cs.MM
http://creativecommons.org/licenses/by-nc-nd/4.0/
High dynamic range (HDR) image is widely-used in graphics and photography due to the rich information it contains. Recently the community has started using deep neural network (DNN) to reconstruct standard dynamic range (SDR) images into HDR. Albeit the superiority of current DNN-based methods, their application scenario is still limited: (1) heavy model impedes real-time processing, and (2) inapplicable to legacy SDR content with more degradation types. Therefore, we propose a lightweight DNN-based method trained to tackle legacy SDR. For better design, we reform the problem modeling and emphasize degradation model. Experiments show that our method reached appealing performance with minimal computational cost compared with others.
[ { "version": "v1", "created": "Mon, 21 Nov 2022 09:05:20 GMT" } ]
2023-03-24T00:00:00
[ [ "Guo", "Cheng", "" ], [ "Jiang", "Xiuhua", "" ] ]
new_dataset
0.984192
2211.13081
Robert Alexander Marsden
Mario D\"obler, Robert A. Marsden, Bin Yang
Robust Mean Teacher for Continual and Gradual Test-Time Adaptation
Accepted at CVPR 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Since experiencing domain shifts during test-time is inevitable in practice, test-time adaption (TTA) continues to adapt the model after deployment. Recently, the area of continual and gradual test-time adaptation (TTA) emerged. In contrast to standard TTA, continual TTA considers not only a single domain shift, but a sequence of shifts. Gradual TTA further exploits the property that some shifts evolve gradually over time. Since in both settings long test sequences are present, error accumulation needs to be addressed for methods relying on self-training. In this work, we propose and show that in the setting of TTA, the symmetric cross-entropy is better suited as a consistency loss for mean teachers compared to the commonly used cross-entropy. This is justified by our analysis with respect to the (symmetric) cross-entropy's gradient properties. To pull the test feature space closer to the source domain, where the pre-trained model is well posed, contrastive learning is leveraged. Since applications differ in their requirements, we address several settings, including having source data available and the more challenging source-free setting. We demonstrate the effectiveness of our proposed method 'robust mean teacher' (RMT) on the continual and gradual corruption benchmarks CIFAR10C, CIFAR100C, and Imagenet-C. We further consider ImageNet-R and propose a new continual DomainNet-126 benchmark. State-of-the-art results are achieved on all benchmarks.
[ { "version": "v1", "created": "Wed, 23 Nov 2022 16:14:45 GMT" }, { "version": "v2", "created": "Wed, 22 Mar 2023 18:44:42 GMT" } ]
2023-03-24T00:00:00
[ [ "Döbler", "Mario", "" ], [ "Marsden", "Robert A.", "" ], [ "Yang", "Bin", "" ] ]
new_dataset
0.967295
2211.14068
Zhian Liu
Zhian Liu, Maomao Li, Yong Zhang, Cairong Wang, Qi Zhang, Jue Wang, Yongwei Nie
Fine-Grained Face Swapping via Regional GAN Inversion
Accepted to CVPR 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present a novel paradigm for high-fidelity face swapping that faithfully preserves the desired subtle geometry and texture details. We rethink face swapping from the perspective of fine-grained face editing, \textit{i.e., ``editing for swapping'' (E4S)}, and propose a framework that is based on the explicit disentanglement of the shape and texture of facial components. Following the E4S principle, our framework enables both global and local swapping of facial features, as well as controlling the amount of partial swapping specified by the user. Furthermore, the E4S paradigm is inherently capable of handling facial occlusions by means of facial masks. At the core of our system lies a novel Regional GAN Inversion (RGI) method, which allows the explicit disentanglement of shape and texture. It also allows face swapping to be performed in the latent space of StyleGAN. Specifically, we design a multi-scale mask-guided encoder to project the texture of each facial component into regional style codes. We also design a mask-guided injection module to manipulate the feature maps with the style codes. Based on the disentanglement, face swapping is reformulated as a simplified problem of style and mask swapping. Extensive experiments and comparisons with current state-of-the-art methods demonstrate the superiority of our approach in preserving texture and shape details, as well as working with high resolution images. The project page is http://e4s2022.github.io
[ { "version": "v1", "created": "Fri, 25 Nov 2022 12:40:45 GMT" }, { "version": "v2", "created": "Thu, 23 Mar 2023 08:05:52 GMT" } ]
2023-03-24T00:00:00
[ [ "Liu", "Zhian", "" ], [ "Li", "Maomao", "" ], [ "Zhang", "Yong", "" ], [ "Wang", "Cairong", "" ], [ "Zhang", "Qi", "" ], [ "Wang", "Jue", "" ], [ "Nie", "Yongwei", "" ] ]
new_dataset
0.970301
2211.14086
Jingwang Ling
Jingwang Ling, Zhibo Wang, Feng Xu
ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervision
CVPR 2023. Project page: https://gerwang.github.io/shadowneus/
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
By supervising camera rays between a scene and multi-view image planes, NeRF reconstructs a neural scene representation for the task of novel view synthesis. On the other hand, shadow rays between the light source and the scene have yet to be considered. Therefore, we propose a novel shadow ray supervision scheme that optimizes both the samples along the ray and the ray location. By supervising shadow rays, we successfully reconstruct a neural SDF of the scene from single-view images under multiple lighting conditions. Given single-view binary shadows, we train a neural network to reconstruct a complete scene not limited by the camera's line of sight. By further modeling the correlation between the image colors and the shadow rays, our technique can also be effectively extended to RGB inputs. We compare our method with previous works on challenging tasks of shape reconstruction from single-view binary shadow or RGB images and observe significant improvements. The code and data are available at https://github.com/gerwang/ShadowNeuS.
[ { "version": "v1", "created": "Fri, 25 Nov 2022 13:14:56 GMT" }, { "version": "v2", "created": "Thu, 23 Mar 2023 14:21:24 GMT" } ]
2023-03-24T00:00:00
[ [ "Ling", "Jingwang", "" ], [ "Wang", "Zhibo", "" ], [ "Xu", "Feng", "" ] ]
new_dataset
0.997946
2212.07597
Emery Berger
Emery D. Berger and Sam Stern and Juan Altmayer Pizzorno
Triangulating Python Performance Issues with Scalene
null
null
null
Accepted, to appear at OSDI 2023
cs.PL cs.PF
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper proposes Scalene, a profiler specialized for Python. Scalene combines a suite of innovations to precisely and simultaneously profile CPU, memory, and GPU usage, all with low overhead. Scalene's CPU and memory profilers help Python programmers direct their optimization efforts by distinguishing between inefficient Python and efficient native execution time and memory usage. Scalene's memory profiler employs a novel sampling algorithm that lets it operate with low overhead yet high precision. It also incorporates a novel algorithm that automatically pinpoints memory leaks, whether within Python or across the Python-native boundary. Scalene tracks a new metric called copy volume, which highlights costly copying operations that can occur when Python silently converts between C and Python data representations, or between CPU and GPU. Since its introduction, Scalene has been widely adopted, with over 500,000 downloads to date. We present experience reports from developers who used Scalene to achieve significant performance improvements and memory savings.
[ { "version": "v1", "created": "Thu, 15 Dec 2022 02:56:25 GMT" } ]
2023-03-24T00:00:00
[ [ "Berger", "Emery D.", "" ], [ "Stern", "Sam", "" ], [ "Pizzorno", "Juan Altmayer", "" ] ]
new_dataset
0.976437
2302.01392
Yiming Sun
Yiming Sun, Bing Cao, Pengfei Zhu, Qinghua Hu
Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Infrared and visible image fusion aims to integrate comprehensive information from multiple sources to achieve superior performances on various practical tasks, such as detection, over that of a single modality. However, most existing methods directly combined the texture details and object contrast of different modalities, ignoring the dynamic changes in reality, which diminishes the visible texture in good lighting conditions and the infrared contrast in low lighting conditions. To fill this gap, we propose a dynamic image fusion framework with a multi-modal gated mixture of local-to-global experts, termed MoE-Fusion, to dynamically extract effective and comprehensive information from the respective modalities. Our model consists of a Mixture of Local Experts (MoLE) and a Mixture of Global Experts (MoGE) guided by a multi-modal gate. The MoLE performs specialized learning of multi-modal local features, prompting the fused images to retain the local information in a sample-adaptive manner, while the MoGE focuses on the global information that complements the fused image with overall texture detail and contrast. Extensive experiments show that our MoE-Fusion outperforms state-of-the-art methods in preserving multi-modal image texture and contrast through the local-to-global dynamic learning paradigm, and also achieves superior performance on detection tasks. Our code will be available: https://github.com/SunYM2020/MoE-Fusion.
[ { "version": "v1", "created": "Thu, 2 Feb 2023 20:06:58 GMT" }, { "version": "v2", "created": "Thu, 23 Mar 2023 07:15:53 GMT" } ]
2023-03-24T00:00:00
[ [ "Sun", "Yiming", "" ], [ "Cao", "Bing", "" ], [ "Zhu", "Pengfei", "" ], [ "Hu", "Qinghua", "" ] ]
new_dataset
0.983856
2302.01703
Tony Han
Fuzhang Han, Han Zheng, Wenjun Huang, Rong Xiong, Yue Wang, Yanmei Jiao
DAMS-LIO: A Degeneration-Aware and Modular Sensor-Fusion LiDAR-inertial Odometry
null
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
With robots being deployed in increasingly complex environments like underground mines and planetary surfaces, the multi-sensor fusion method has gained more and more attention which is a promising solution to state estimation in the such scene. The fusion scheme is a central component of these methods. In this paper, a light-weight iEKF-based LiDAR-inertial odometry system is presented, which utilizes a degeneration-aware and modular sensor-fusion pipeline that takes both LiDAR points and relative pose from another odometry as the measurement in the update process only when degeneration is detected. Both the Cramer-Rao Lower Bound (CRLB) theory and simulation test are used to demonstrate the higher accuracy of our method compared to methods using a single observation. Furthermore, the proposed system is evaluated in perceptually challenging datasets against various state-of-the-art sensor-fusion methods. The results show that the proposed system achieves real-time and high estimation accuracy performance despite the challenging environment and poor observations.
[ { "version": "v1", "created": "Fri, 3 Feb 2023 13:01:55 GMT" }, { "version": "v2", "created": "Wed, 8 Feb 2023 14:09:06 GMT" }, { "version": "v3", "created": "Thu, 23 Mar 2023 06:02:59 GMT" } ]
2023-03-24T00:00:00
[ [ "Han", "Fuzhang", "" ], [ "Zheng", "Han", "" ], [ "Huang", "Wenjun", "" ], [ "Xiong", "Rong", "" ], [ "Wang", "Yue", "" ], [ "Jiao", "Yanmei", "" ] ]
new_dataset
0.973073
2303.04238
Raz Lapid
Raz Lapid and Moshe Sipper
Patch of Invisibility: Naturalistic Black-Box Adversarial Attacks on Object Detectors
null
null
null
null
cs.CV cs.AI cs.NE
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Adversarial attacks on deep-learning models have been receiving increased attention in recent years. Work in this area has mostly focused on gradient-based techniques, so-called white-box attacks, wherein the attacker has access to the targeted model's internal parameters; such an assumption is usually unrealistic in the real world. Some attacks additionally use the entire pixel space to fool a given model, which is neither practical nor physical (i.e., real-world). On the contrary, we propose herein a gradient-free method that uses the learned image manifold of a pretrained generative adversarial network (GAN) to generate naturalistic physical adversarial patches for object detectors. We show that our proposed method works both digitally and physically.
[ { "version": "v1", "created": "Tue, 7 Mar 2023 21:03:48 GMT" }, { "version": "v2", "created": "Thu, 9 Mar 2023 11:14:06 GMT" }, { "version": "v3", "created": "Thu, 23 Mar 2023 08:49:30 GMT" } ]
2023-03-24T00:00:00
[ [ "Lapid", "Raz", "" ], [ "Sipper", "Moshe", "" ] ]
new_dataset
0.990132
2303.12692
Benjamin Kenwright
Benjamin Kenwright
Dual-Quaternions: Theory and Applications in Sound
null
null
null
null
cs.SD eess.AS
http://creativecommons.org/licenses/by/4.0/
Sound is a fundamental and rich source of information; playing a key role in many areas from humanities and social sciences through to engineering and mathematics. Sound is more than just data 'signals'. It encapsulates physical, sensorial and emotional, as well as social, cultural and environmental factors. Sound contributes to the transformation of our experiences, environments and beliefs. Sound is all around us and everywhere. Hence, it should come as no surprise that sound is a complex multicomponent entity with a vast assortment of characteristics and applications. Of course, an important question is, what is the best way to store and represent sound digitally to capture these characteristics? What model or method is best for manipulating, extracting and filtering sounds? There are a large number of representations and models, however, one approach that has yet to be used with sound is dual-quaternions. While dual-quaternions have established themselves in many fields of science and computing as an efficient mathematical model for providing an unambiguous, un-cumbersome, computationally effective means of representing multi-component data. Sound is one area that has yet to explore and reap the benefits of dual-quaternions (using sound and audio-related dual-quaternion models). This article aims to explore the exciting potential and possibilities dual-quaternions offer when applied and combined with sound-based models (including but not limited to the applications, tools, machine-learning, statistical and computational sound-related algorithms).
[ { "version": "v1", "created": "Wed, 22 Mar 2023 16:40:24 GMT" }, { "version": "v2", "created": "Thu, 23 Mar 2023 17:09:59 GMT" } ]
2023-03-24T00:00:00
[ [ "Kenwright", "Benjamin", "" ] ]
new_dataset
0.999566
2303.12798
Yimin Dai
Yimin Dai and Xian Shuai and Rui Tan and Guoliang Xing
Interpersonal Distance Tracking with mmWave Radar and IMUs
null
null
null
null
cs.NI cs.LG cs.SY eess.SY
http://creativecommons.org/licenses/by-nc-nd/4.0/
Tracking interpersonal distances is essential for real-time social distancing management and {\em ex-post} contact tracing to prevent spreads of contagious diseases. Bluetooth neighbor discovery has been employed for such purposes in combating COVID-19, but does not provide satisfactory spatiotemporal resolutions. This paper presents ImmTrack, a system that uses a millimeter wave radar and exploits the inertial measurement data from user-carried smartphones or wearables to track interpersonal distances. By matching the movement traces reconstructed from the radar and inertial data, the pseudo identities of the inertial data can be transferred to the radar sensing results in the global coordinate system. The re-identified, radar-sensed movement trajectories are then used to track interpersonal distances. In a broader sense, ImmTrack is the first system that fuses data from millimeter wave radar and inertial measurement units for simultaneous user tracking and re-identification. Evaluation with up to 27 people in various indoor/outdoor environments shows ImmTrack's decimeters-seconds spatiotemporal accuracy in contact tracing, which is similar to that of the privacy-intrusive camera surveillance and significantly outperforms the Bluetooth neighbor discovery approach.
[ { "version": "v1", "created": "Tue, 28 Feb 2023 15:44:17 GMT" } ]
2023-03-24T00:00:00
[ [ "Dai", "Yimin", "" ], [ "Shuai", "Xian", "" ], [ "Tan", "Rui", "" ], [ "Xing", "Guoliang", "" ] ]
new_dataset
0.959031
2303.12808
Vaibhav Garg
Vaibhav Garg, Ganning Xu, and Munindar P. Singh
PACO: Provocation Involving Action, Culture, and Oppression
null
null
null
null
cs.CL cs.AI
http://creativecommons.org/licenses/by/4.0/
In India, people identify with a particular group based on certain attributes such as religion. The same religious groups are often provoked against each other. Previous studies show the role of provocation in increasing tensions between India's two prominent religious groups: Hindus and Muslims. With the advent of the Internet, such provocation also surfaced on social media platforms such as WhatsApp. By leveraging an existing dataset of Indian WhatsApp posts, we identified three categories of provoking sentences against Indian Muslims. Further, we labeled 7,000 sentences for three provocation categories and called this dataset PACO. We leveraged PACO to train a model that can identify provoking sentences from a WhatsApp post. Our best model is fine-tuned RoBERTa and achieved a 0.851 average AUC score over five-fold cross-validation. Automatically identifying provoking sentences could stop provoking text from reaching out to the masses, and can prevent possible discrimination or violence against the target religious group. Further, we studied the provocative speech through a pragmatic lens, by identifying the dialog acts and impoliteness super-strategies used against the religious group.
[ { "version": "v1", "created": "Sun, 19 Mar 2023 04:39:36 GMT" } ]
2023-03-24T00:00:00
[ [ "Garg", "Vaibhav", "" ], [ "Xu", "Ganning", "" ], [ "Singh", "Munindar P.", "" ] ]
new_dataset
0.999844
2303.12811
Amani Alshawabka
Amani Al-shawabka, Philip Pietraski, Sudhir B Pattar, Pedram Johari, Tommaso Melodia
SignCRF: Scalable Channel-agnostic Data-driven Radio Authentication System
11 pages, 13 figures, 3 tables
null
null
null
cs.CR cs.AI cs.LG cs.NI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Radio Frequency Fingerprinting through Deep Learning (RFFDL) is a data-driven IoT authentication technique that leverages the unique hardware-level manufacturing imperfections associated with a particular device to recognize (fingerprint) the device based on variations introduced in the transmitted waveform. The proposed SignCRF is a scalable, channel-agnostic, data-driven radio authentication platform with unmatched precision in fingerprinting wireless devices based on their unique manufacturing impairments and independent of the dynamic channel irregularities caused by mobility. SignCRF consists of (i) a baseline classifier finely trained to authenticate devices with high accuracy and at scale; (ii) an environment translator carefully designed and trained to remove the dynamic channel impact from RF signals while maintaining the radio's specific signature; (iii) a Max-Rule module that selects the highest precision authentication technique between the baseline classifier and the environment translator per radio. We design, train, and validate the performance of SignCRF for multiple technologies in dynamic environments and at scale (100 LoRa and 20 WiFi devices). We demonstrate that SignCRF significantly improves the RFFDL performance by achieving as high as 5x and 8x improvement in correct authentication of WiFi and LoRa devices when compared to the state-of-the-art, respectively.
[ { "version": "v1", "created": "Tue, 21 Mar 2023 21:11:02 GMT" } ]
2023-03-24T00:00:00
[ [ "Al-shawabka", "Amani", "" ], [ "Pietraski", "Philip", "" ], [ "Pattar", "Sudhir B", "" ], [ "Johari", "Pedram", "" ], [ "Melodia", "Tommaso", "" ] ]
new_dataset
0.988544
2303.12869
Jessica Nayeli Lopez Espejel
Jessica L\'opez Espejel, Mahaman Sanoussi Yahaya Alassan, Walid Dahhane, El Hassane Ettifouri
JaCoText: A Pretrained Model for Java Code-Text Generation
International Conference on Code Generation and Implementation Volume: 17
Espejel, J. L., Alassan, M. S. Y., Dahhane, W., & Ettifouri, E. H. (2023). JaCoText: A Pretrained Model for Java Code-Text Generation. International Journal of Computer and Systems Engineering, 17(2), 100-105
null
null
cs.CL
http://creativecommons.org/licenses/by-nc-sa/4.0/
Pretrained transformer-based models have shown high performance in natural language generation task. However, a new wave of interest has surged: automatic programming language generation. This task consists of translating natural language instructions to a programming code. Despite the fact that well-known pretrained models on language generation have achieved good performance in learning programming languages, effort is still needed in automatic code generation. In this paper, we introduce JaCoText, a model based on Transformers neural network. It aims to generate java source code from natural language text. JaCoText leverages advantages of both natural language and code generation models. More specifically, we study some findings from the state of the art and use them to (1) initialize our model from powerful pretrained models, (2) explore additional pretraining on our java dataset, (3) carry out experiments combining the unimodal and bimodal data in the training, and (4) scale the input and output length during the fine-tuning of the model. Conducted experiments on CONCODE dataset show that JaCoText achieves new state-of-the-art results.
[ { "version": "v1", "created": "Wed, 22 Mar 2023 19:01:25 GMT" } ]
2023-03-24T00:00:00
[ [ "Espejel", "Jessica López", "" ], [ "Alassan", "Mahaman Sanoussi Yahaya", "" ], [ "Dahhane", "Walid", "" ], [ "Ettifouri", "El Hassane", "" ] ]
new_dataset
0.996622
2303.12889
Zijin Wang
Ou Zheng, Mohamed Abdel-Aty, Zijin Wang, Shengxuan Ding, Dongdong Wang, Yuxuan Huang
AVOID: Autonomous Vehicle Operation Incident Dataset Across the Globe
null
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Crash data of autonomous vehicles (AV) or vehicles equipped with advanced driver assistance systems (ADAS) are the key information to understand the crash nature and to enhance the automation systems. However, most of the existing crash data sources are either limited by the sample size or suffer from missing or unverified data. To contribute to the AV safety research community, we introduce AVOID: an open AV crash dataset. Three types of vehicles are considered: Advanced Driving System (ADS) vehicles, Advanced Driver Assistance Systems (ADAS) vehicles, and low-speed autonomous shuttles. The crash data are collected from the National Highway Traffic Safety Administration (NHTSA), California Department of Motor Vehicles (CA DMV) and incident news worldwide, and the data are manually verified and summarized in ready-to-use format. In addition, land use, weather, and geometry information are also provided. The dataset is expected to accelerate the research on AV crash analysis and potential risk identification by providing the research community with data of rich samples, diverse data sources, clear data structure, and high data quality.
[ { "version": "v1", "created": "Wed, 22 Mar 2023 20:05:23 GMT" } ]
2023-03-24T00:00:00
[ [ "Zheng", "Ou", "" ], [ "Abdel-Aty", "Mohamed", "" ], [ "Wang", "Zijin", "" ], [ "Ding", "Shengxuan", "" ], [ "Wang", "Dongdong", "" ], [ "Huang", "Yuxuan", "" ] ]
new_dataset
0.999295
2303.12890
Djemel Ziou
Aicha Baya Goumeidane, Djemel Ziou, and Nafaa Nacereddine
Scale space radon transform-based inertia axis and object central symmetry estimation
This work has not been published
null
null
null
cs.CV eess.IV
http://creativecommons.org/licenses/by/4.0/
Inertia Axes are involved in many techniques for image content measurement when involving information obtained from lines, angles, centroids... etc. We investigate, here, the estimation of the main axis of inertia of an object in the image. We identify the coincidence conditions of the Scale Space Radon Transform (SSRT) maximum and the inertia main axis. We show, that by choosing the appropriate scale parameter, it is possible to match the SSRT maximum and the main axis of inertia location and orientation of the embedded object in the image. Furthermore, an example of use case is presented where binary objects central symmetry computation is derived by means of SSRT projections and the axis of inertia orientation. To this end, some SSRT characteristics have been highlighted and exploited. The experimentations show the SSRT-based main axis of inertia computation effectiveness. Concerning the central symmetry, results are very satisfying as experimentations carried out on randomly created images dataset and existing datasets have permitted to divide successfully these images bases into centrally symmetric and non-centrally symmetric objects.
[ { "version": "v1", "created": "Wed, 22 Mar 2023 20:07:27 GMT" } ]
2023-03-24T00:00:00
[ [ "Goumeidane", "Aicha Baya", "" ], [ "Ziou", "Djemel", "" ], [ "Nacereddine", "Nafaa", "" ] ]
new_dataset
0.999729
2303.12892
Thanh Dung Le
Thanh-Dung Le, Philippe Jouvet, Rita Noumeir
A Small-Scale Switch Transformer and NLP-based Model for Clinical Narratives Classification
Submitted to IEEE Journal of Biomedical and Health Informatics
null
null
null
cs.CL eess.SP
http://creativecommons.org/licenses/by-nc-nd/4.0/
In recent years, Transformer-based models such as the Switch Transformer have achieved remarkable results in natural language processing tasks. However, these models are often too complex and require extensive pre-training, which limits their effectiveness for small clinical text classification tasks with limited data. In this study, we propose a simplified Switch Transformer framework and train it from scratch on a small French clinical text classification dataset at CHU Sainte-Justine hospital. Our results demonstrate that the simplified small-scale Transformer models outperform pre-trained BERT-based models, including DistillBERT, CamemBERT, FlauBERT, and FrALBERT. Additionally, using a mixture of expert mechanisms from the Switch Transformer helps capture diverse patterns; hence, the proposed approach achieves better results than a conventional Transformer with the self-attention mechanism. Finally, our proposed framework achieves an accuracy of 87\%, precision at 87\%, and recall at 85\%, compared to the third-best pre-trained BERT-based model, FlauBERT, which achieved an accuracy of 84\%, precision at 84\%, and recall at 84\%. However, Switch Transformers have limitations, including a generalization gap and sharp minima. We compare it with a multi-layer perceptron neural network for small French clinical narratives classification and show that the latter outperforms all other models.
[ { "version": "v1", "created": "Wed, 22 Mar 2023 20:10:29 GMT" } ]
2023-03-24T00:00:00
[ [ "Le", "Thanh-Dung", "" ], [ "Jouvet", "Philippe", "" ], [ "Noumeir", "Rita", "" ] ]
new_dataset
0.982193
2303.12940
Ehsan Nowroozi
Ehsan Nowroozi, Seyedsadra Seyedshoari, Yassine Mekdad, Erkay Savas, Mauro Conti
Cryptocurrency wallets: assessment and security
null
null
null
null
cs.CR cs.CV cs.DC cs.IT math.IT
http://creativecommons.org/licenses/by/4.0/
Digital wallet as a software program or a digital device allows users to conduct various transactions. Hot and cold digital wallets are considered as two types of this wallet. Digital wallets need an online connection fall into the first group, whereas digital wallets can operate without internet connection belong to the second group. Prior to buying a digital wallet, it is important to define for what purpose it will be utilized. The ease with which a mobile phone transaction may be completed in a couple of seconds and the speed with which transactions are executed are reflection of efficiency. One of the most important elements of digital wallets is data organization. Digital wallets are significantly less expensive than classic methods of transaction, which entails various charges and fees. Constantly, demand for their usage is growing due to speed, security, and the ability to conduct transactions between two users without the need of a third party. As the popularity of digital currency wallets grows, the number of security concerns impacting them increases significantly. The current status of digital wallets on the market, as well as the options for an efficient solution for obtaining and utilizing digital wallets. Finally, the digital wallets' security and future improvement prospects are discussed in this chapter.
[ { "version": "v1", "created": "Mon, 6 Mar 2023 08:52:01 GMT" } ]
2023-03-24T00:00:00
[ [ "Nowroozi", "Ehsan", "" ], [ "Seyedshoari", "Seyedsadra", "" ], [ "Mekdad", "Yassine", "" ], [ "Savas", "Erkay", "" ], [ "Conti", "Mauro", "" ] ]
new_dataset
0.999806
2303.12946
Jinchao Zhu
Feng Dong, Jinchao Zhu
Underwater Camouflage Object Detection Dataset
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We have made a dataset of camouflage object detection mainly for complex seabed scenes, and named it UnderWater RGB&Sonar,or UW-RS for short. The UW-RS dataset contains a total of 1972 image data. The dataset mainly consists of two parts, namely underwater optical data part (UW-R dataset) and underwater sonar data part (UW-S dataset).
[ { "version": "v1", "created": "Wed, 1 Mar 2023 22:36:54 GMT" } ]
2023-03-24T00:00:00
[ [ "Dong", "Feng", "" ], [ "Zhu", "Jinchao", "" ] ]
new_dataset
0.999825
2303.12984
Teerapat Jenrungrot
Teerapat Jenrungrot and Michael Chinen and W. Bastiaan Kleijn and Jan Skoglund and Zal\'an Borsos and Neil Zeghidour and Marco Tagliasacchi
LMCodec: A Low Bitrate Speech Codec With Causal Transformer Models
5 pages, accepted to ICASSP 2023, project page: https://mjenrungrot.github.io/chrome-media-audio-papers/publications/lmcodec
null
null
null
cs.SD eess.AS
http://creativecommons.org/licenses/by/4.0/
We introduce LMCodec, a causal neural speech codec that provides high quality audio at very low bitrates. The backbone of the system is a causal convolutional codec that encodes audio into a hierarchy of coarse-to-fine tokens using residual vector quantization. LMCodec trains a Transformer language model to predict the fine tokens from the coarse ones in a generative fashion, allowing for the transmission of fewer codes. A second Transformer predicts the uncertainty of the next codes given the past transmitted codes, and is used to perform conditional entropy coding. A MUSHRA subjective test was conducted and shows that the quality is comparable to reference codecs at higher bitrates. Example audio is available at https://mjenrungrot.github.io/chrome-media-audio-papers/publications/lmcodec.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 01:27:38 GMT" } ]
2023-03-24T00:00:00
[ [ "Jenrungrot", "Teerapat", "" ], [ "Chinen", "Michael", "" ], [ "Kleijn", "W. Bastiaan", "" ], [ "Skoglund", "Jan", "" ], [ "Borsos", "Zalán", "" ], [ "Zeghidour", "Neil", "" ], [ "Tagliasacchi", "Marco", "" ] ]
new_dataset
0.991648
2303.13013
Nan Gao
Nan Gao, Zeyu Zhao, Zhi Zeng, Shuwu Zhang, Dongdong Weng
GesGPT: Speech Gesture Synthesis With Text Parsing from GPT
null
null
null
null
cs.CL cs.CV cs.HC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Gesture synthesis has gained significant attention as a critical research area, focusing on producing contextually appropriate and natural gestures corresponding to speech or textual input. Although deep learning-based approaches have achieved remarkable progress, they often overlook the rich semantic information present in the text, leading to less expressive and meaningful gestures. We propose GesGPT, a novel approach to gesture generation that leverages the semantic analysis capabilities of Large Language Models (LLMs), such as GPT. By capitalizing on the strengths of LLMs for text analysis, we design prompts to extract gesture-related information from textual input. Our method entails developing prompt principles that transform gesture generation into an intention classification problem based on GPT, and utilizing a curated gesture library and integration module to produce semantically rich co-speech gestures. Experimental results demonstrate that GesGPT effectively generates contextually appropriate and expressive gestures, offering a new perspective on semantic co-speech gesture generation.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 03:30:30 GMT" } ]
2023-03-24T00:00:00
[ [ "Gao", "Nan", "" ], [ "Zhao", "Zeyu", "" ], [ "Zeng", "Zhi", "" ], [ "Zhang", "Shuwu", "" ], [ "Weng", "Dongdong", "" ] ]
new_dataset
0.958232
2303.13018
Yunsong Zhou
Yunsong Zhou, Hongzi Zhu, Quan Liu, Shan Chang, and Minyi Guo
MonoATT: Online Monocular 3D Object Detection with Adaptive Token Transformer
in the Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Mobile monocular 3D object detection (Mono3D) (e.g., on a vehicle, a drone, or a robot) is an important yet challenging task. Existing transformer-based offline Mono3D models adopt grid-based vision tokens, which is suboptimal when using coarse tokens due to the limited available computational power. In this paper, we propose an online Mono3D framework, called MonoATT, which leverages a novel vision transformer with heterogeneous tokens of varying shapes and sizes to facilitate mobile Mono3D. The core idea of MonoATT is to adaptively assign finer tokens to areas of more significance before utilizing a transformer to enhance Mono3D. To this end, we first use prior knowledge to design a scoring network for selecting the most important areas of the image, and then propose a token clustering and merging network with an attention mechanism to gradually merge tokens around the selected areas in multiple stages. Finally, a pixel-level feature map is reconstructed from heterogeneous tokens before employing a SOTA Mono3D detector as the underlying detection core. Experiment results on the real-world KITTI dataset demonstrate that MonoATT can effectively improve the Mono3D accuracy for both near and far objects and guarantee low latency. MonoATT yields the best performance compared with the state-of-the-art methods by a large margin and is ranked number one on the KITTI 3D benchmark.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 03:45:03 GMT" } ]
2023-03-24T00:00:00
[ [ "Zhou", "Yunsong", "" ], [ "Zhu", "Hongzi", "" ], [ "Liu", "Quan", "" ], [ "Chang", "Shan", "" ], [ "Guo", "Minyi", "" ] ]
new_dataset
0.991729
2303.13019
Jinnan Piao
Jinnan Piao, Dong Li, Jindi Liu, Xueting Yu, Zhibo Li, Ming Yang, and Peng Zeng
Construction Methods Based Minimum Weight Distribution for Polar Codes with Successive Cancellation List Decoding
null
null
null
null
cs.IT math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we focus on the construction methods based MWD for polar codes to improve the performance with successive cancellation list (SCL) decoding. We first propose an ordered and nested reliability sequence, namely MWD sequence, to improve the ML performance of polar codes and apply fast construction without the original channel information. In the MWD sequence, the synthetic channels are sorted by the partial MWD which is used to evaluate the influence of information bit on MWD and we prove the MWD sequence is the optimum sequence under ML decoding. Then, since the list size of SCL decoding is limited, we introduce an entropy constraint to establish a relationship between the list size and the ML performance and propose a heuristic and greedy construction method named bit grouping reorder based MWD (BGR-MWD) algorithm. In the algorithm, we divide the synthetic channels into groups by the partial MWD and greedily reorder the synthetic channels in some groups until the entropy constraint is satisfied. The simulation results show the MWD sequence is suitable for constructing polar codes with short code length. Meanwhile, the BGR-MWD algorithm has superior performance over the traditional construction methods for long code length.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 03:48:58 GMT" } ]
2023-03-24T00:00:00
[ [ "Piao", "Jinnan", "" ], [ "Li", "Dong", "" ], [ "Liu", "Jindi", "" ], [ "Yu", "Xueting", "" ], [ "Li", "Zhibo", "" ], [ "Yang", "Ming", "" ], [ "Zeng", "Peng", "" ] ]
new_dataset
0.987831
2303.13026
Maryam Babaie
Maryam Babaie, Ayaz Akram, Jason Lowe-Power
A Cycle-level Unified DRAM Cache Controller Model for 3DXPoint Memory Systems in gem5
null
null
null
null
cs.AR cs.PF
http://creativecommons.org/licenses/by/4.0/
To accommodate the growing memory footprints of today's applications, CPU vendors have employed large DRAM caches, backed by large non-volatile memories like Intel Optane (e.g., Intel's Cascade Lake). The existing computer architecture simulators do not provide support to model and evaluate systems which use DRAM devices as a cache to the non-volatile main memory. In this work, we present a cycle-level DRAM cache model which is integrated with gem5. This model leverages the flexibility of gem5's memory devices models and full system support to enable exploration of many different DRAM cache designs. We demonstrate the usefulness of this new tool by exploring the design space of a DRAM cache controller through several case studies including the impact of scheduling policies, required buffering, combining different memory technologies (e.g., HBM, DDR3/4/5, 3DXPoint, High latency) as the cache and main memory, and the effect of wear-leveling when DRAM cache is backed by NVM main memory. We also perform experiments with real workloads in full-system simulations to validate the proposed model and show the sensitivity of these workloads to the DRAM cache sizes.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 04:24:30 GMT" } ]
2023-03-24T00:00:00
[ [ "Babaie", "Maryam", "" ], [ "Akram", "Ayaz", "" ], [ "Lowe-Power", "Jason", "" ] ]
new_dataset
0.999375
2303.13071
Sizhe An
Sizhe An, Hongyi Xu, Yichun Shi, Guoxian Song, Umit Ogras, Linjie Luo
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360$^{\circ}$
CVPR 2023. Project Page:https://sizhean.github.io/panohead
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Synthesis and reconstruction of 3D human head has gained increasing interests in computer vision and computer graphics recently. Existing state-of-the-art 3D generative adversarial networks (GANs) for 3D human head synthesis are either limited to near-frontal views or hard to preserve 3D consistency in large view angles. We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in $360^\circ$ with diverse appearance and detailed geometry using only in-the-wild unstructured images for training. At its core, we lift up the representation power of recent 3D GANs and bridge the data alignment gap when training from in-the-wild images with widely distributed views. Specifically, we propose a novel two-stage self-adaptive image alignment for robust 3D GAN training. We further introduce a tri-grid neural volume representation that effectively addresses front-face and back-head feature entanglement rooted in the widely-adopted tri-plane formulation. Our method instills prior knowledge of 2D image segmentation in adversarial learning of 3D neural scene structures, enabling compositable head synthesis in diverse backgrounds. Benefiting from these designs, our method significantly outperforms previous 3D GANs, generating high-quality 3D heads with accurate geometry and diverse appearances, even with long wavy and afro hairstyles, renderable from arbitrary poses. Furthermore, we show that our system can reconstruct full 3D heads from single input images for personalized realistic 3D avatars.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 06:54:34 GMT" } ]
2023-03-24T00:00:00
[ [ "An", "Sizhe", "" ], [ "Xu", "Hongyi", "" ], [ "Shi", "Yichun", "" ], [ "Song", "Guoxian", "" ], [ "Ogras", "Umit", "" ], [ "Luo", "Linjie", "" ] ]
new_dataset
0.967623
2303.13076
Xiaoshi Wu
Xiaoshi Wu, Feng Zhu, Rui Zhao, Hongsheng Li
CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching
11 pages, 4 figures. Accepted by CVPR 2023
null
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by/4.0/
Open-vocabulary detection (OVD) is an object detection task aiming at detecting objects from novel categories beyond the base categories on which the detector is trained. Recent OVD methods rely on large-scale visual-language pre-trained models, such as CLIP, for recognizing novel objects. We identify the two core obstacles that need to be tackled when incorporating these models into detector training: (1) the distribution mismatch that happens when applying a VL-model trained on whole images to region recognition tasks; (2) the difficulty of localizing objects of unseen classes. To overcome these obstacles, we propose CORA, a DETR-style framework that adapts CLIP for Open-vocabulary detection by Region prompting and Anchor pre-matching. Region prompting mitigates the whole-to-region distribution gap by prompting the region features of the CLIP-based region classifier. Anchor pre-matching helps learning generalizable object localization by a class-aware matching mechanism. We evaluate CORA on the COCO OVD benchmark, where we achieve 41.7 AP50 on novel classes, which outperforms the previous SOTA by 2.4 AP50 even without resorting to extra training data. When extra training data is available, we train CORA$^+$ on both ground-truth base-category annotations and additional pseudo bounding box labels computed by CORA. CORA$^+$ achieves 43.1 AP50 on the COCO OVD benchmark and 28.1 box APr on the LVIS OVD benchmark.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 07:13:57 GMT" } ]
2023-03-24T00:00:00
[ [ "Wu", "Xiaoshi", "" ], [ "Zhu", "Feng", "" ], [ "Zhao", "Rui", "" ], [ "Li", "Hongsheng", "" ] ]
new_dataset
0.99934
2303.13100
Yun Liu
Yun Liu, Xuefeng Yan, Zhilei Chen, Zhiqi Li, Zeyong Wei, and Mingqiang Wei
PointGame: Geometrically and Adaptively Masked Auto-Encoder on Point Clouds
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Self-supervised learning is attracting large attention in point cloud understanding. However, exploring discriminative and transferable features still remains challenging due to their nature of irregularity and sparsity. We propose a geometrically and adaptively masked auto-encoder for self-supervised learning on point clouds, termed \textit{PointGame}. PointGame contains two core components: GATE and EAT. GATE stands for the geometrical and adaptive token embedding module; it not only absorbs the conventional wisdom of geometric descriptors that captures the surface shape effectively, but also exploits adaptive saliency to focus on the salient part of a point cloud. EAT stands for the external attention-based Transformer encoder with linear computational complexity, which increases the efficiency of the whole pipeline. Unlike cutting-edge unsupervised learning models, PointGame leverages geometric descriptors to perceive surface shapes and adaptively mines discriminative features from training data. PointGame showcases clear advantages over its competitors on various downstream tasks under both global and local fine-tuning strategies. The code and pre-trained models will be publicly available.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 08:32:10 GMT" } ]
2023-03-24T00:00:00
[ [ "Liu", "Yun", "" ], [ "Yan", "Xuefeng", "" ], [ "Chen", "Zhilei", "" ], [ "Li", "Zhiqi", "" ], [ "Wei", "Zeyong", "" ], [ "Wei", "Mingqiang", "" ] ]
new_dataset
0.998852
2303.13182
Zhengping Che
Mingze Wei, Yaomin Huang, Zhiyuan Xu, Ning Liu, Zhengping Che, Xinyu Zhang, Chaomin Shen, Feifei Feng, Chun Shan, Jian Tang
CMG-Net: An End-to-End Contact-Based Multi-Finger Dexterous Grasping Network
The first two authors are with equal contributions. Paper accepted by ICRA 2023
null
null
null
cs.RO cs.AI cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we propose a novel representation for grasping using contacts between multi-finger robotic hands and objects to be manipulated. This representation significantly reduces the prediction dimensions and accelerates the learning process. We present an effective end-to-end network, CMG-Net, for grasping unknown objects in a cluttered environment by efficiently predicting multi-finger grasp poses and hand configurations from a single-shot point cloud. Moreover, we create a synthetic grasp dataset that consists of five thousand cluttered scenes, 80 object categories, and 20 million annotations. We perform a comprehensive empirical study and demonstrate the effectiveness of our grasping representation and CMG-Net. Our work significantly outperforms the state-of-the-art for three-finger robotic hands. We also demonstrate that the model trained using synthetic data performs very well for real robots.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 11:29:31 GMT" } ]
2023-03-24T00:00:00
[ [ "Wei", "Mingze", "" ], [ "Huang", "Yaomin", "" ], [ "Xu", "Zhiyuan", "" ], [ "Liu", "Ning", "" ], [ "Che", "Zhengping", "" ], [ "Zhang", "Xinyu", "" ], [ "Shen", "Chaomin", "" ], [ "Feng", "Feifei", "" ], [ "Shan", "Chun", "" ], [ "Tang", "Jian", "" ] ]
new_dataset
0.999805
2303.13194
Yunkang Cao
Yunkang Cao, Xiaohao Xu, Weiming Shen
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection
Submitted to Pattern Recognition. Code is available on https://github.com/caoyunkang/CPMF
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Point cloud (PCD) anomaly detection steadily emerges as a promising research area. This study aims to improve PCD anomaly detection performance by combining handcrafted PCD descriptions with powerful pre-trained 2D neural networks. To this end, this study proposes Complementary Pseudo Multimodal Feature (CPMF) that incorporates local geometrical information in 3D modality using handcrafted PCD descriptors and global semantic information in the generated pseudo 2D modality using pre-trained 2D neural networks. For global semantics extraction, CPMF projects the origin PCD into a pseudo 2D modality containing multi-view images. These images are delivered to pre-trained 2D neural networks for informative 2D modality feature extraction. The 3D and 2D modality features are aggregated to obtain the CPMF for PCD anomaly detection. Extensive experiments demonstrate the complementary capacity between 2D and 3D modality features and the effectiveness of CPMF, with 95.15% image-level AU-ROC and 92.93% pixel-level PRO on the MVTec3D benchmark. Code is available on https://github.com/caoyunkang/CPMF.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 11:52:17 GMT" } ]
2023-03-24T00:00:00
[ [ "Cao", "Yunkang", "" ], [ "Xu", "Xiaohao", "" ], [ "Shen", "Weiming", "" ] ]
new_dataset
0.963744
2303.13254
EPTCS
Ana Cruz (University of Aveiro), Alexandre Madeira (University of Aveiro), Lu\^A-\~A-s Soares Barbosa (University of Minho)
Paraconsistent Transition Systems
In Proceedings LSFA 2022, arXiv:2303.12680
EPTCS 376, 2023, pp. 3-15
10.4204/EPTCS.376.3
null
cs.LO
http://creativecommons.org/licenses/by/4.0/
Often in Software Engineering, a modeling formalism has to support scenarios of inconsistency in which several requirements either reinforce or contradict each other. Paraconsistent transition systems are proposed in this paper as one such formalism: states evolve through two accessibility relations capturing weighted evidence of a transition or its absence, respectively. Their weights come from a specific residuated lattice. A category of these systems, and the corresponding algebra, is defined as providing a formal setting to model different application scenarios. One of them, dealing with the effect of quantum decoherence in quantum programs, is used for illustration purposes.
[ { "version": "v1", "created": "Thu, 23 Mar 2023 13:37:49 GMT" } ]
2023-03-24T00:00:00
[ [ "Cruz", "Ana", "", "University of Aveiro" ], [ "Madeira", "Alexandre", "", "University of\n Aveiro" ], [ "Barbosa", "LuÂ-Ã-s Soares", "", "University of Minho" ] ]
new_dataset
0.999561