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2302.00988
Xiaozheng Zheng
Xiaozheng Zheng, Chao Wen, Zhou Xue, Pengfei Ren, Jingyu Wang
HaMuCo: Hand Pose Estimation via Multiview Collaborative Self-Supervised Learning
Accepted to ICCV 2023. Won first place in the HANDS22 Challenge Task 2. Project page: https://zxz267.github.io/HaMuCo
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recent advancements in 3D hand pose estimation have shown promising results, but its effectiveness has primarily relied on the availability of large-scale annotated datasets, the creation of which is a laborious and costly process. To alleviate the label-hungry limitation, we propose a self-supervised learning framework, HaMuCo, that learns a single-view hand pose estimator from multi-view pseudo 2D labels. However, one of the main challenges of self-supervised learning is the presence of noisy labels and the ``groupthink'' effect from multiple views. To overcome these issues, we introduce a cross-view interaction network that distills the single-view estimator by utilizing the cross-view correlated features and enforcing multi-view consistency to achieve collaborative learning. Both the single-view estimator and the cross-view interaction network are trained jointly in an end-to-end manner. Extensive experiments show that our method can achieve state-of-the-art performance on multi-view self-supervised hand pose estimation. Furthermore, the proposed cross-view interaction network can also be applied to hand pose estimation from multi-view input and outperforms previous methods under the same settings.
[ { "version": "v1", "created": "Thu, 2 Feb 2023 10:13:04 GMT" }, { "version": "v2", "created": "Tue, 15 Aug 2023 04:51:27 GMT" } ]
2023-08-16T00:00:00
[ [ "Zheng", "Xiaozheng", "" ], [ "Wen", "Chao", "" ], [ "Xue", "Zhou", "" ], [ "Ren", "Pengfei", "" ], [ "Wang", "Jingyu", "" ] ]
new_dataset
0.997485
2302.12449
Yun Zhu
Yun Zhu and Jianhao Guo and Siliang Tang
SGL-PT: A Strong Graph Learner with Graph Prompt Tuning
null
null
null
null
cs.LG cs.AI cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Recently, much exertion has been paid to design graph self-supervised methods to obtain generalized pre-trained models, and adapt pre-trained models onto downstream tasks through fine-tuning. However, there exists an inherent gap between pretext and downstream graph tasks, which insufficiently exerts the ability of pre-trained models and even leads to negative transfer. Meanwhile, prompt tuning has seen emerging success in natural language processing by aligning pre-training and fine-tuning with consistent training objectives. In this paper, we identify the challenges for graph prompt tuning: The first is the lack of a strong and universal pre-training task across sundry pre-training methods in graph domain. The second challenge lies in the difficulty of designing a consistent training objective for both pre-training and downstream tasks. To overcome above obstacles, we propose a novel framework named SGL-PT which follows the learning strategy ``Pre-train, Prompt, and Predict''. Specifically, we raise a strong and universal pre-training task coined as SGL that acquires the complementary merits of generative and contrastive self-supervised graph learning. And aiming for graph classification task, we unify pre-training and fine-tuning by designing a novel verbalizer-free prompting function, which reformulates the downstream task in a similar format as pretext task. Empirical results show that our method surpasses other baselines under unsupervised setting, and our prompt tuning method can greatly facilitate models on biological datasets over fine-tuning methods.
[ { "version": "v1", "created": "Fri, 24 Feb 2023 04:31:18 GMT" }, { "version": "v2", "created": "Tue, 15 Aug 2023 08:11:16 GMT" } ]
2023-08-16T00:00:00
[ [ "Zhu", "Yun", "" ], [ "Guo", "Jianhao", "" ], [ "Tang", "Siliang", "" ] ]
new_dataset
0.992402
2302.14325
Lun Luo
Lun Luo, Shuhang Zheng, Yixuan Li, Yongzhi Fan, Beinan Yu, Siyuan Cao, Huiliang Shen
BEVPlace: Learning LiDAR-based Place Recognition using Bird's Eye View Images
Accepted by ICCV 2023
null
null
null
cs.CV cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Place recognition is a key module for long-term SLAM systems. Current LiDAR-based place recognition methods usually use representations of point clouds such as unordered points or range images. These methods achieve high recall rates of retrieval, but their performance may degrade in the case of view variation or scene changes. In this work, we explore the potential of a different representation in place recognition, i.e. bird's eye view (BEV) images. We observe that the structural contents of BEV images are less influenced by rotations and translations of point clouds. We validate that, without any delicate design, a simple VGGNet trained on BEV images achieves comparable performance with the state-of-the-art place recognition methods in scenes of slight viewpoint changes. For more robust place recognition, we design a rotation-invariant network called BEVPlace. We use group convolution to extract rotation-equivariant local features from the images and NetVLAD for global feature aggregation. In addition, we observe that the distance between BEV features is correlated with the geometry distance of point clouds. Based on the observation, we develop a method to estimate the position of the query cloud, extending the usage of place recognition. The experiments conducted on large-scale public datasets show that our method 1) achieves state-of-the-art performance in terms of recall rates, 2) is robust to view changes, 3) shows strong generalization ability, and 4) can estimate the positions of query point clouds. Source codes are publicly available at https://github.com/zjuluolun/BEVPlace.
[ { "version": "v1", "created": "Tue, 28 Feb 2023 05:37:45 GMT" }, { "version": "v2", "created": "Wed, 15 Mar 2023 02:38:54 GMT" }, { "version": "v3", "created": "Tue, 15 Aug 2023 03:44:00 GMT" } ]
2023-08-16T00:00:00
[ [ "Luo", "Lun", "" ], [ "Zheng", "Shuhang", "" ], [ "Li", "Yixuan", "" ], [ "Fan", "Yongzhi", "" ], [ "Yu", "Beinan", "" ], [ "Cao", "Siyuan", "" ], [ "Shen", "Huiliang", "" ] ]
new_dataset
0.980352
2303.05234
Yang Fu
Yang Fu, Shibei Meng, Saihui Hou, Xuecai Hu and Yongzhen Huang
GPGait: Generalized Pose-based Gait Recognition
ICCV Camera Ready
null
null
null
cs.CV
http://creativecommons.org/licenses/by-nc-nd/4.0/
Recent works on pose-based gait recognition have demonstrated the potential of using such simple information to achieve results comparable to silhouette-based methods. However, the generalization ability of pose-based methods on different datasets is undesirably inferior to that of silhouette-based ones, which has received little attention but hinders the application of these methods in real-world scenarios. To improve the generalization ability of pose-based methods across datasets, we propose a \textbf{G}eneralized \textbf{P}ose-based \textbf{Gait} recognition (\textbf{GPGait}) framework. First, a Human-Oriented Transformation (HOT) and a series of Human-Oriented Descriptors (HOD) are proposed to obtain a unified pose representation with discriminative multi-features. Then, given the slight variations in the unified representation after HOT and HOD, it becomes crucial for the network to extract local-global relationships between the keypoints. To this end, a Part-Aware Graph Convolutional Network (PAGCN) is proposed to enable efficient graph partition and local-global spatial feature extraction. Experiments on four public gait recognition datasets, CASIA-B, OUMVLP-Pose, Gait3D and GREW, show that our model demonstrates better and more stable cross-domain capabilities compared to existing skeleton-based methods, achieving comparable recognition results to silhouette-based ones. Code is available at https://github.com/BNU-IVC/FastPoseGait.
[ { "version": "v1", "created": "Thu, 9 Mar 2023 13:17:13 GMT" }, { "version": "v2", "created": "Tue, 15 Aug 2023 07:32:29 GMT" } ]
2023-08-16T00:00:00
[ [ "Fu", "Yang", "" ], [ "Meng", "Shibei", "" ], [ "Hou", "Saihui", "" ], [ "Hu", "Xuecai", "" ], [ "Huang", "Yongzhen", "" ] ]
new_dataset
0.953517
2303.05648
Qingming Li
Qingming Li and H. Vicky Zhao
Pacos: Modeling Users' Interpretable and Context-Dependent Choices in Preference Reversals
29 pages, 12 figures
null
10.1016/j.knosys.2023.110835
null
cs.IR cs.AI cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Choice problems refer to selecting the best choices from several items, and learning users' preferences in choice problems is of great significance in understanding the decision making mechanisms and providing personalized services. Existing works typically assume that people evaluate items independently. In practice, however, users' preferences depend on the market in which items are placed, which is known as context effects; and the order of users' preferences for two items may even be reversed, which is referred to preference reversals. In this work, we identify three factors contributing to context effects: users' adaptive weights, the inter-item comparison, and display positions. We propose a context-dependent preference model named Pacos as a unified framework for addressing three factors simultaneously, and consider two design methods including an additive method with high interpretability and an ANN-based method with high accuracy. We study the conditions for preference reversals to occur and provide an theoretical proof of the effectiveness of Pacos in addressing preference reversals. Experimental results show that the proposed method has better performance than prior works in predicting users' choices, and has great interpretability to help understand the cause of preference reversals.
[ { "version": "v1", "created": "Fri, 10 Mar 2023 01:49:56 GMT" }, { "version": "v2", "created": "Sun, 18 Jun 2023 03:40:40 GMT" } ]
2023-08-16T00:00:00
[ [ "Li", "Qingming", "" ], [ "Zhao", "H. Vicky", "" ] ]
new_dataset
0.985973
2303.06445
Mojtaba Esfandiari
Soroush Sadeghnejad, Mojtaba Esfandiari and Farshad Khadivar
A Virtual-Based Haptic Endoscopic Sinus Surgery (ESS) Training System: from Development to Validation
null
null
10.1016/B978-0-443-18460-4.00002-0
null
cs.RO
http://creativecommons.org/licenses/by/4.0/
Simulated training platforms offer a suitable avenue for surgical students and professionals to build and improve upon their skills, without the hassle of traditional training methods. To enhance the degree of realistic interaction paradigms of training simulators, great work has been done to both model simulated anatomy in more realistic fashion, as well as providing appropriate haptic feedback to the trainee. As such, this chapter seeks to discuss the ongoing research being conducted on haptic feedback-incorporated simulators specifically for Endoscopic Sinus Surgery (ESS). This chapter offers a brief comparative analysis of some EES simulators, in addition to a deeper quantitative and qualitative look into our approach to designing and prototyping a complete virtual-based haptic EES training platform.
[ { "version": "v1", "created": "Sat, 11 Mar 2023 16:46:57 GMT" } ]
2023-08-16T00:00:00
[ [ "Sadeghnejad", "Soroush", "" ], [ "Esfandiari", "Mojtaba", "" ], [ "Khadivar", "Farshad", "" ] ]
new_dataset
0.980296
2303.18232
Ximeng Sun
Ximeng Sun, Pengchuan Zhang, Peizhao Zhang, Hardik Shah, Kate Saenko, Xide Xia
DIME-FM: DIstilling Multimodal and Efficient Foundation Models
Accepted to ICCV 2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Large Vision-Language Foundation Models (VLFM), such as CLIP, ALIGN and Florence, are trained on large-scale datasets of image-caption pairs and achieve superior transferability and robustness on downstream tasks, but they are difficult to use in many practical applications due to their large size, high latency and fixed architectures. Unfortunately, recent work shows training a small custom VLFM for resource-limited applications is currently very difficult using public and smaller-scale data. In this paper, we introduce a new distillation mechanism (DIME-FM) that allows us to transfer the knowledge contained in large VLFMs to smaller, customized foundation models using a relatively small amount of inexpensive, unpaired images and sentences. We transfer the knowledge from the pre-trained CLIP-ViTL/14 model to a ViT-B/32 model, with only 40M public images and 28.4M unpaired public sentences. The resulting model "Distill-ViT-B/32" rivals the CLIP-ViT-B/32 model pre-trained on its private WiT dataset (400M image-text pairs): Distill-ViT-B/32 achieves similar results in terms of zero-shot and linear-probing performance on both ImageNet and the ELEVATER (20 image classification tasks) benchmarks. It also displays comparable robustness when evaluated on five datasets with natural distribution shifts from ImageNet.
[ { "version": "v1", "created": "Fri, 31 Mar 2023 17:47:23 GMT" }, { "version": "v2", "created": "Mon, 14 Aug 2023 18:30:40 GMT" } ]
2023-08-16T00:00:00
[ [ "Sun", "Ximeng", "" ], [ "Zhang", "Pengchuan", "" ], [ "Zhang", "Peizhao", "" ], [ "Shah", "Hardik", "" ], [ "Saenko", "Kate", "" ], [ "Xia", "Xide", "" ] ]
new_dataset
0.996518
2304.03251
Bjoern Michele
Bjoern Michele, Alexandre Boulch, Gilles Puy, Tuan-Hung Vu, Renaud Marlet, Nicolas Courty
SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation
Project repository: github.com/valeoai/SALUDA
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Learning models on one labeled dataset that generalize well on another domain is a difficult task, as several shifts might happen between the data domains. This is notably the case for lidar data, for which models can exhibit large performance discrepancies due for instance to different lidar patterns or changes in acquisition conditions. This paper addresses the corresponding Unsupervised Domain Adaptation (UDA) task for semantic segmentation. To mitigate this problem, we introduce an unsupervised auxiliary task of learning an implicit underlying surface representation simultaneously on source and target data. As both domains share the same latent representation, the model is forced to accommodate discrepancies between the two sources of data. This novel strategy differs from classical minimization of statistical divergences or lidar-specific domain adaptation techniques. Our experiments demonstrate that our method achieves a better performance than the current state of the art, both in real-to-real and synthetic-to-real scenarios.
[ { "version": "v1", "created": "Thu, 6 Apr 2023 17:36:23 GMT" }, { "version": "v2", "created": "Tue, 15 Aug 2023 12:31:33 GMT" } ]
2023-08-16T00:00:00
[ [ "Michele", "Bjoern", "" ], [ "Boulch", "Alexandre", "" ], [ "Puy", "Gilles", "" ], [ "Vu", "Tuan-Hung", "" ], [ "Marlet", "Renaud", "" ], [ "Courty", "Nicolas", "" ] ]
new_dataset
0.997558
2304.11463
Samuel Schulter
Samuel Schulter, Vijay Kumar B G, Yumin Suh, Konstantinos M. Dafnis, Zhixing Zhang, Shiyu Zhao, Dimitris Metaxas
OmniLabel: A Challenging Benchmark for Language-Based Object Detection
ICCV 2023 Oral - Visit our project website at https://www.omnilabel.org
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Language-based object detection is a promising direction towards building a natural interface to describe objects in images that goes far beyond plain category names. While recent methods show great progress in that direction, proper evaluation is lacking. With OmniLabel, we propose a novel task definition, dataset, and evaluation metric. The task subsumes standard- and open-vocabulary detection as well as referring expressions. With more than 28K unique object descriptions on over 25K images, OmniLabel provides a challenging benchmark with diverse and complex object descriptions in a naturally open-vocabulary setting. Moreover, a key differentiation to existing benchmarks is that our object descriptions can refer to one, multiple or even no object, hence, providing negative examples in free-form text. The proposed evaluation handles the large label space and judges performance via a modified average precision metric, which we validate by evaluating strong language-based baselines. OmniLabel indeed provides a challenging test bed for future research on language-based detection.
[ { "version": "v1", "created": "Sat, 22 Apr 2023 18:35:50 GMT" }, { "version": "v2", "created": "Mon, 14 Aug 2023 21:43:42 GMT" } ]
2023-08-16T00:00:00
[ [ "Schulter", "Samuel", "" ], [ "G", "Vijay Kumar B", "" ], [ "Suh", "Yumin", "" ], [ "Dafnis", "Konstantinos M.", "" ], [ "Zhang", "Zhixing", "" ], [ "Zhao", "Shiyu", "" ], [ "Metaxas", "Dimitris", "" ] ]
new_dataset
0.999299
2305.06794
Zhiheng Li
Zhiheng Li, Yubo Cui, Yu Lin, Zheng Fang
MMF-Track: Multi-modal Multi-level Fusion for 3D Single Object Tracking
11 pages, 10 figures
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
3D single object tracking plays a crucial role in computer vision. Mainstream methods mainly rely on point clouds to achieve geometry matching between target template and search area. However, textureless and incomplete point clouds make it difficult for single-modal trackers to distinguish objects with similar structures. To overcome the limitations of geometry matching, we propose a Multi-modal Multi-level Fusion Tracker (MMF-Track), which exploits the image texture and geometry characteristic of point clouds to track 3D target. Specifically, we first propose a Space Alignment Module (SAM) to align RGB images with point clouds in 3D space, which is the prerequisite for constructing inter-modal associations. Then, in feature interaction level, we design a Feature Interaction Module (FIM) based on dual-stream structure, which enhances intra-modal features in parallel and constructs inter-modal semantic associations. Meanwhile, in order to refine each modal feature, we introduce a Coarse-to-Fine Interaction Module (CFIM) to realize the hierarchical feature interaction at different scales. Finally, in similarity fusion level, we propose a Similarity Fusion Module (SFM) to aggregate geometry and texture clues from the target. Experiments show that our method achieves state-of-the-art performance on KITTI (39% Success and 42% Precision gains against previous multi-modal method) and is also competitive on NuScenes.
[ { "version": "v1", "created": "Thu, 11 May 2023 13:34:02 GMT" }, { "version": "v2", "created": "Tue, 15 Aug 2023 03:24:57 GMT" } ]
2023-08-16T00:00:00
[ [ "Li", "Zhiheng", "" ], [ "Cui", "Yubo", "" ], [ "Lin", "Yu", "" ], [ "Fang", "Zheng", "" ] ]
new_dataset
0.998392
2307.00360
Zuchao Li
Zuchao Li, Shitou Zhang, Hai Zhao, Yifei Yang, Dongjie Yang
BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformer
null
null
null
null
cs.CL
http://creativecommons.org/licenses/by/4.0/
BatGPT is a large-scale language model designed and trained jointly by Wuhan University and Shanghai Jiao Tong University. It is capable of generating highly natural and fluent text in response to various types of input, including text prompts, images, and audio. In the modeling level, we employ a bidirectional autoregressive architecture that allows the model to efficiently capture the complex dependencies of natural language, making it highly effective in tasks such as language generation, dialog systems, and question answering. Moreover, the bidirectional autoregressive modeling not only operates from left to right but also from right to left, effectively reducing fixed memory effects and alleviating model hallucinations. In the training aspect, we propose a novel parameter expansion method for leveraging the pre-training of smaller models and employ reinforcement learning from both AI and human feedback, aimed at improving the model's alignment performance. Overall, these approaches significantly improve the effectiveness of BatGPT, and the model can be utilized for a wide range of natural language applications.
[ { "version": "v1", "created": "Sat, 1 Jul 2023 15:10:01 GMT" }, { "version": "v2", "created": "Tue, 15 Aug 2023 13:59:42 GMT" } ]
2023-08-16T00:00:00
[ [ "Li", "Zuchao", "" ], [ "Zhang", "Shitou", "" ], [ "Zhao", "Hai", "" ], [ "Yang", "Yifei", "" ], [ "Yang", "Dongjie", "" ] ]
new_dataset
0.998523
2307.15958
Maksym Bekuzarov
Maksym Bekuzarov, Ariana Bermudez, Joon-Young Lee, Hao Li
XMem++: Production-level Video Segmentation From Few Annotated Frames
Accepted to ICCV 2023. 18 pages, 16 figures
null
null
null
cs.CV cs.GR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Despite advancements in user-guided video segmentation, extracting complex objects consistently for highly complex scenes is still a labor-intensive task, especially for production. It is not uncommon that a majority of frames need to be annotated. We introduce a novel semi-supervised video object segmentation (SSVOS) model, XMem++, that improves existing memory-based models, with a permanent memory module. Most existing methods focus on single frame annotations, while our approach can effectively handle multiple user-selected frames with varying appearances of the same object or region. Our method can extract highly consistent results while keeping the required number of frame annotations low. We further introduce an iterative and attention-based frame suggestion mechanism, which computes the next best frame for annotation. Our method is real-time and does not require retraining after each user input. We also introduce a new dataset, PUMaVOS, which covers new challenging use cases not found in previous benchmarks. We demonstrate SOTA performance on challenging (partial and multi-class) segmentation scenarios as well as long videos, while ensuring significantly fewer frame annotations than any existing method. Project page: https://max810.github.io/xmem2-project-page/
[ { "version": "v1", "created": "Sat, 29 Jul 2023 11:18:23 GMT" }, { "version": "v2", "created": "Tue, 15 Aug 2023 11:26:36 GMT" } ]
2023-08-16T00:00:00
[ [ "Bekuzarov", "Maksym", "" ], [ "Bermudez", "Ariana", "" ], [ "Lee", "Joon-Young", "" ], [ "Li", "Hao", "" ] ]
new_dataset
0.995366
2308.01246
Jyotirmaya Shivottam Mr.
Jyotirmaya Shivottam and Subhankar Mishra
Tirtha -- An Automated Platform to Crowdsource Images and Create 3D Models of Heritage Sites
Accepted at The 28th International ACM Conference on 3D Web Technology (Web3D 2023)
null
10.1145/3611314.3615904
null
cs.CV cs.HC cs.LG
http://creativecommons.org/licenses/by-nc-sa/4.0/
Digital preservation of Cultural Heritage (CH) sites is crucial to protect them against damage from natural disasters or human activities. Creating 3D models of CH sites has become a popular method of digital preservation thanks to advancements in computer vision and photogrammetry. However, the process is time-consuming, expensive, and typically requires specialized equipment and expertise, posing challenges in resource-limited developing countries. Additionally, the lack of an open repository for 3D models hinders research and public engagement with their heritage. To address these issues, we propose Tirtha, a web platform for crowdsourcing images of CH sites and creating their 3D models. Tirtha utilizes state-of-the-art Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques. It is modular, extensible and cost-effective, allowing for the incorporation of new techniques as photogrammetry advances. Tirtha is accessible through a web interface at https://tirtha.niser.ac.in and can be deployed on-premise or in a cloud environment. In our case studies, we demonstrate the pipeline's effectiveness by creating 3D models of temples in Odisha, India, using crowdsourced images. These models are available for viewing, interaction, and download on the Tirtha website. Our work aims to provide a dataset of crowdsourced images and 3D reconstructions for research in computer vision, heritage conservation, and related domains. Overall, Tirtha is a step towards democratizing digital preservation, primarily in resource-limited developing countries.
[ { "version": "v1", "created": "Wed, 2 Aug 2023 16:00:39 GMT" }, { "version": "v2", "created": "Tue, 15 Aug 2023 17:39:05 GMT" } ]
2023-08-16T00:00:00
[ [ "Shivottam", "Jyotirmaya", "" ], [ "Mishra", "Subhankar", "" ] ]
new_dataset
0.998849
2308.01413
Tiezhu Sun
Tiezhu Sun, Weiguo Pian, Nadia Daoudi, Kevin Allix, Tegawend\'e F. Bissyand\'e, Jacques Klein
LaFiCMIL: Rethinking Large File Classification from the Perspective of Correlated Multiple Instance Learning
12 pages; update results; manuscript revision
null
null
null
cs.CL cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Transformer-based models, such as BERT, have revolutionized various language tasks, but still struggle with large file classification due to their input limit (e.g., 512 tokens). Despite several attempts to alleviate this limitation, no method consistently excels across all benchmark datasets, primarily because they can only extract partial essential information from the input file. Additionally, they fail to adapt to the varied properties of different types of large files. In this work, we tackle this problem from the perspective of correlated multiple instance learning. The proposed approach, LaFiCMIL, serves as a versatile framework applicable to various large file classification tasks covering binary, multi-class, and multi-label classification tasks, spanning various domains including Natural Language Processing, Programming Language Processing, and Android Analysis. To evaluate its effectiveness, we employ eight benchmark datasets pertaining to Long Document Classification, Code Defect Detection, and Android Malware Detection. Leveraging BERT-family models as feature extractors, our experimental results demonstrate that LaFiCMIL achieves new state-of-the-art performance across all benchmark datasets. This is largely attributable to its capability of scaling BERT up to nearly 20K tokens, running on a single Tesla V-100 GPU with 32G of memory.
[ { "version": "v1", "created": "Sun, 30 Jul 2023 18:47:54 GMT" }, { "version": "v2", "created": "Tue, 15 Aug 2023 12:19:56 GMT" } ]
2023-08-16T00:00:00
[ [ "Sun", "Tiezhu", "" ], [ "Pian", "Weiguo", "" ], [ "Daoudi", "Nadia", "" ], [ "Allix", "Kevin", "" ], [ "Bissyandé", "Tegawendé F.", "" ], [ "Klein", "Jacques", "" ] ]
new_dataset
0.977641
2308.02158
Jiaxin Chen
Xin Liao and Siliang Chen and Jiaxin Chen and Tianyi Wang and Xiehua Li
CTP-Net: Character Texture Perception Network for Document Image Forgery Localization
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Due to the progression of information technology in recent years, document images have been widely disseminated on social networks. With the help of powerful image editing tools, document images are easily forged without leaving visible manipulation traces, which leads to severe issues if significant information is falsified for malicious use. Therefore, the research of document image forensics is worth further exploring. In this paper, we propose a Character Texture Perception Network (CTP-Net) to localize the forged regions in document images. Specifically, considering the characters with semantics in a document image are highly vulnerable, capturing the forgery traces is the key to localize the forged regions. We design a Character Texture Stream (CTS) based on optical character recognition to capture features of text areas that are essential components of a document image. Meanwhile, texture features of the whole document image are exploited by an Image Texture Stream (ITS). Combining the features extracted from the CTS and the ITS, the CTP-Net can reveal more subtle forgery traces from document images. Moreover, to overcome the challenge caused by the lack of fake document images, we design a data generation strategy that is utilized to construct a Fake Chinese Trademark dataset (FCTM). Experimental results on different datasets demonstrate that the proposed CTP-Net is able to localize multi-scale forged areas in document images, and outperform the state-of-the-art forgery localization methods, even though post-processing operations are applied.
[ { "version": "v1", "created": "Fri, 4 Aug 2023 06:37:28 GMT" }, { "version": "v2", "created": "Tue, 15 Aug 2023 03:45:50 GMT" } ]
2023-08-16T00:00:00
[ [ "Liao", "Xin", "" ], [ "Chen", "Siliang", "" ], [ "Chen", "Jiaxin", "" ], [ "Wang", "Tianyi", "" ], [ "Li", "Xiehua", "" ] ]
new_dataset
0.989883
2308.07207
Mufeng Yao
Mufeng Yao, Jiaqi Wang, Jinlong Peng, Mingmin Chi, Chao Liu
FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow
Accepted by ACM Multi-Media 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Multiple object tracking (MOT) has been successfully investigated in computer vision. However, MOT for the videos captured by unmanned aerial vehicles (UAV) is still challenging due to small object size, blurred object appearance, and very large and/or irregular motion in both ground objects and UAV platforms. In this paper, we propose FOLT to mitigate these problems and reach fast and accurate MOT in UAV view. Aiming at speed-accuracy trade-off, FOLT adopts a modern detector and light-weight optical flow extractor to extract object detection features and motion features at a minimum cost. Given the extracted flow, the flow-guided feature augmentation is designed to augment the object detection feature based on its optical flow, which improves the detection of small objects. Then the flow-guided motion prediction is also proposed to predict the object's position in the next frame, which improves the tracking performance of objects with very large displacements between adjacent frames. Finally, the tracker matches the detected objects and predicted objects using a spatially matching scheme to generate tracks for every object. Experiments on Visdrone and UAVDT datasets show that our proposed model can successfully track small objects with large and irregular motion and outperform existing state-of-the-art methods in UAV-MOT tasks.
[ { "version": "v1", "created": "Mon, 14 Aug 2023 15:24:44 GMT" }, { "version": "v2", "created": "Tue, 15 Aug 2023 02:59:04 GMT" } ]
2023-08-16T00:00:00
[ [ "Yao", "Mufeng", "" ], [ "Wang", "Jiaqi", "" ], [ "Peng", "Jinlong", "" ], [ "Chi", "Mingmin", "" ], [ "Liu", "Chao", "" ] ]
new_dataset
0.958429
2308.07340
Naresh Goud Boddu
Rishabh Batra, Naresh Goud Boddu, Rahul Jain
Quantum secure non-malleable randomness encoder and its applications
arXiv admin note: text overlap with arXiv:2308.06466
null
null
null
cs.CR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
"Non-Malleable Randomness Encoder"(NMRE) was introduced by Kanukurthi, Obbattu, and Sekar~[KOS18] as a useful cryptographic primitive helpful in the construction of non-malleable codes. To the best of our knowledge, their construction is not known to be quantum secure. We provide a construction of a first rate-$1/2$, $2$-split, quantum secure NMRE and use this in a black-box manner, to construct for the first time the following: 1) rate $1/11$, $3$-split, quantum non-malleable code, 2) rate $1/3$, $3$-split, quantum secure non-malleable code, 3) rate $1/5$, $2$-split, average case quantum secure non-malleable code.
[ { "version": "v1", "created": "Sat, 12 Aug 2023 05:23:44 GMT" } ]
2023-08-16T00:00:00
[ [ "Batra", "Rishabh", "" ], [ "Boddu", "Naresh Goud", "" ], [ "Jain", "Rahul", "" ] ]
new_dataset
0.992057
2308.07346
Joseph Ramsey
Joseph D. Ramsey, Bryan Andrews
Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search
Causal Analysis Workshop Series (CAWS) 2023, 12 pages, 4 Figures, 2 Tables
null
null
null
cs.MS cs.AI cs.PL
http://creativecommons.org/licenses/by/4.0/
We give novel Python and R interfaces for the (Java) Tetrad project for causal modeling, search, and estimation. The Tetrad project is a mainstay in the literature, having been under consistent development for over 30 years. Some of its algorithms are now classics, like PC and FCI; others are recent developments. It is increasingly the case, however, that researchers need to access the underlying Java code from Python or R. Existing methods for doing this are inadequate. We provide new, up-to-date methods using the JPype Python-Java interface and the Reticulate Python-R interface, directly solving these issues. With the addition of some simple tools and the provision of working examples for both Python and R, using JPype and Reticulate to interface Python and R with Tetrad is straightforward and intuitive.
[ { "version": "v1", "created": "Sun, 13 Aug 2023 16:29:05 GMT" } ]
2023-08-16T00:00:00
[ [ "Ramsey", "Joseph D.", "" ], [ "Andrews", "Bryan", "" ] ]
new_dataset
0.965682
2308.07391
Jiayi Liu
Jiayi Liu, Ali Mahdavi-Amiri, Manolis Savva
PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects
Presented at ICCV 2023. Project website: https://3dlg-hcvc.github.io/paris/
null
null
null
cs.CV cs.AI cs.GR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We address the task of simultaneous part-level reconstruction and motion parameter estimation for articulated objects. Given two sets of multi-view images of an object in two static articulation states, we decouple the movable part from the static part and reconstruct shape and appearance while predicting the motion parameters. To tackle this problem, we present PARIS: a self-supervised, end-to-end architecture that learns part-level implicit shape and appearance models and optimizes motion parameters jointly without any 3D supervision, motion, or semantic annotation. Our experiments show that our method generalizes better across object categories, and outperforms baselines and prior work that are given 3D point clouds as input. Our approach improves reconstruction relative to state-of-the-art baselines with a Chamfer-L1 distance reduction of 3.94 (45.2%) for objects and 26.79 (84.5%) for parts, and achieves 5% error rate for motion estimation across 10 object categories. Video summary at: https://youtu.be/tDSrROPCgUc
[ { "version": "v1", "created": "Mon, 14 Aug 2023 18:18:00 GMT" } ]
2023-08-16T00:00:00
[ [ "Liu", "Jiayi", "" ], [ "Mahdavi-Amiri", "Ali", "" ], [ "Savva", "Manolis", "" ] ]
new_dataset
0.999326
2308.07427
Jane Hsieh
Jane Hsieh, Joselyn Kim, Laura Dabbish, Haiyi Zhu
Nip it in the Bud: Moderation Strategies in Open Source Software Projects and the Role of Bots
null
null
10.1145/3610092
null
cs.HC cs.SE
http://creativecommons.org/licenses/by/4.0/
Much of our modern digital infrastructure relies critically upon open sourced software. The communities responsible for building this cyberinfrastructure require maintenance and moderation, which is often supported by volunteer efforts. Moderation, as a non-technical form of labor, is a necessary but often overlooked task that maintainers undertake to sustain the community around an OSS project. This study examines the various structures and norms that support community moderation, describes the strategies moderators use to mitigate conflicts, and assesses how bots can play a role in assisting these processes. We interviewed 14 practitioners to uncover existing moderation practices and ways that automation can provide assistance. Our main contributions include a characterization of moderated content in OSS projects, moderation techniques, as well as perceptions of and recommendations for improving the automation of moderation tasks. We hope that these findings will inform the implementation of more effective moderation practices in open source communities.
[ { "version": "v1", "created": "Mon, 14 Aug 2023 19:42:51 GMT" } ]
2023-08-16T00:00:00
[ [ "Hsieh", "Jane", "" ], [ "Kim", "Joselyn", "" ], [ "Dabbish", "Laura", "" ], [ "Zhu", "Haiyi", "" ] ]
new_dataset
0.967903
2308.07449
Aiman Soliman
Aiman Soliman, Priyam Mazumdar, Aaron Hoyle-Katz, Brian Allan, and Allison Gardner
Integrated dataset for air travel and reported Zika virus cases in Colombia (Data and Resources Paper)
null
null
null
null
cs.SI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This open-access dataset provides consistent records of air travel volumes between 205 airport catchments in Colombia and the associated number of reported human cases of Zika virus within these catchments during the arbovirus outbreak between October 2015 and September 2016. We associated in this dataset the monthly air travel volumes provided by the Colombian Civil Aviation Authority (AEROCIVIL) with the reported human cases of Zika Virus published by The Pan American Health Organization (PAHO). Our methodology consists of geocoding all the reported airports and identifying the catchment of each airport using the municipalities' boundaries since reported human cases of Zika Virus are available at the municipal level. In addition, we calculated the total population at risk in each airport catchment by combining the total population count in a catchment with the environmental suitability of the Aedes aegypti mosquito, the vector for the Zika virus. We separated the monthly air travel volumes into domestic and international based on the location of the origin airport. The current dataset includes the total air travel volumes of 23,539,364 passengers on domestic flights and 11,592,197 on international ones. We validated our dataset by comparing the monthly aggregated air travel volumes between airport catchments to those predicted by the gravity model. We hope the novel dataset will provide a resource to researchers studying the role of human mobility in the spread of mosquito-borne diseases and modeling disease spread in realistic networks.
[ { "version": "v1", "created": "Mon, 14 Aug 2023 20:38:58 GMT" } ]
2023-08-16T00:00:00
[ [ "Soliman", "Aiman", "" ], [ "Mazumdar", "Priyam", "" ], [ "Hoyle-Katz", "Aaron", "" ], [ "Allan", "Brian", "" ], [ "Gardner", "Allison", "" ] ]
new_dataset
0.999501
2308.07472
Chinmay Chinara
Thomas B Talbot and Chinmay Chinara
Open Medical Gesture: An Open-Source Experiment in Naturalistic Physical Interactions for Mixed and Virtual Reality Simulations
AHFE 2022
Human Factors in Virtual Environments and Game Design. AHFE (2022) International Conference. AHFE Open Access, vol 50, 1-7. AHFE International, USA
10.54941/ahfe1002054
null
cs.HC
http://creativecommons.org/licenses/by/4.0/
Mixed Reality (MR) and Virtual Reality (VR) simulations are hampered by requirements for hand controllers or attempts to perseverate in use of two-dimensional computer interface paradigms from the 1980s. From our efforts to produce more naturalistic interactions for combat medic training for the military, USC has developed an open-source toolkit that enables direct hand controlled responsive interactions that is sensor independent and can function with depth sensing cameras, webcams or sensory gloves. Natural approaches we have examined include the ability to manipulate virtual smart objects in a similar manner to how they are used in the real world. From this research and review of current literature, we have discerned several best approaches for hand-based human computer interactions which provide intuitive, responsive, useful, and low frustration experiences for VR users.
[ { "version": "v1", "created": "Mon, 14 Aug 2023 21:56:41 GMT" } ]
2023-08-16T00:00:00
[ [ "Talbot", "Thomas B", "" ], [ "Chinara", "Chinmay", "" ] ]
new_dataset
0.994346
2308.07498
Wenguan Wang
Hanqing Wang, Wei Liang, Luc Van Gool, Wenguan Wang
DREAMWALKER: Mental Planning for Continuous Vision-Language Navigation
Accepted at ICCV 2023; Project page: https://github.com/hanqingwangai/Dreamwalker
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
VLN-CE is a recently released embodied task, where AI agents need to navigate a freely traversable environment to reach a distant target location, given language instructions. It poses great challenges due to the huge space of possible strategies. Driven by the belief that the ability to anticipate the consequences of future actions is crucial for the emergence of intelligent and interpretable planning behavior, we propose DREAMWALKER -- a world model based VLN-CE agent. The world model is built to summarize the visual, topological, and dynamic properties of the complicated continuous environment into a discrete, structured, and compact representation. DREAMWALKER can simulate and evaluate possible plans entirely in such internal abstract world, before executing costly actions. As opposed to existing model-free VLN-CE agents simply making greedy decisions in the real world, which easily results in shortsighted behaviors, DREAMWALKER is able to make strategic planning through large amounts of ``mental experiments.'' Moreover, the imagined future scenarios reflect our agent's intention, making its decision-making process more transparent. Extensive experiments and ablation studies on VLN-CE dataset confirm the effectiveness of the proposed approach and outline fruitful directions for future work.
[ { "version": "v1", "created": "Mon, 14 Aug 2023 23:45:01 GMT" } ]
2023-08-16T00:00:00
[ [ "Wang", "Hanqing", "" ], [ "Liang", "Wei", "" ], [ "Van Gool", "Luc", "" ], [ "Wang", "Wenguan", "" ] ]
new_dataset
0.997301
2308.07502
Varun Viswanath
Yinan Xuan, Varun Viswanath, Sunny Chu, Owen Bartolf, Jessica Echterhoff, and Edward Wang
SpecTracle: Wearable Facial Motion Tracking from Unobtrusive Peripheral Cameras
null
null
null
null
cs.HC cs.CV
http://creativecommons.org/licenses/by/4.0/
Facial motion tracking in head-mounted displays (HMD) has the potential to enable immersive "face-to-face" interaction in a virtual environment. However, current works on facial tracking are not suitable for unobtrusive augmented reality (AR) glasses or do not have the ability to track arbitrary facial movements. In this work, we demonstrate a novel system called SpecTracle that tracks a user's facial motions using two wide-angle cameras mounted right next to the visor of a Hololens. Avoiding the usage of cameras extended in front of the face, our system greatly improves the feasibility to integrate full-face tracking into a low-profile form factor. We also demonstrate that a neural network-based model processing the wide-angle cameras can run in real-time at 24 frames per second (fps) on a mobile GPU and track independent facial movement for different parts of the face with a user-independent model. Using a short personalized calibration, the system improves its tracking performance by 42.3% compared to the user-independent model.
[ { "version": "v1", "created": "Mon, 14 Aug 2023 23:52:19 GMT" } ]
2023-08-16T00:00:00
[ [ "Xuan", "Yinan", "" ], [ "Viswanath", "Varun", "" ], [ "Chu", "Sunny", "" ], [ "Bartolf", "Owen", "" ], [ "Echterhoff", "Jessica", "" ], [ "Wang", "Edward", "" ] ]
new_dataset
0.999397
2308.07512
Ans Qureshi
Ans Qureshi, David Smith, Trevor Gee, Mahla Nejati, Jalil Shahabi, JongYoon Lim, Ho Seok Ahn, Ben McGuinness, Catherine Downes, Rahul Jangali, Kale Black, Hin Lim, Mike Duke, Bruce MacDonald, Henry Williams
Seeing the Fruit for the Leaves: Robotically Mapping Apple Fruitlets in a Commercial Orchard
Accepted at the International Conference on Intelligent Robots and Systems (IROS 2023)
null
null
null
cs.RO
http://creativecommons.org/licenses/by-nc-sa/4.0/
Aotearoa New Zealand has a strong and growing apple industry but struggles to access workers to complete skilled, seasonal tasks such as thinning. To ensure effective thinning and make informed decisions on a per-tree basis, it is crucial to accurately measure the crop load of individual apple trees. However, this task poses challenges due to the dense foliage that hides the fruitlets within the tree structure. In this paper, we introduce the vision system of an automated apple fruitlet thinning robot, developed to tackle the labor shortage issue. This paper presents the initial design, implementation,and evaluation specifics of the system. The platform straddles the 3.4 m tall 2D apple canopy structures to create an accurate map of the fruitlets on each tree. We show that this platform can measure the fruitlet load on an apple tree by scanning through both sides of the branch. The requirement of an overarching platform was justified since two-sided scans had a higher counting accuracy of 81.17 % than one-sided scans at 73.7 %. The system was also demonstrated to produce size estimates within 5.9% RMSE of their true size.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 00:33:26 GMT" } ]
2023-08-16T00:00:00
[ [ "Qureshi", "Ans", "" ], [ "Smith", "David", "" ], [ "Gee", "Trevor", "" ], [ "Nejati", "Mahla", "" ], [ "Shahabi", "Jalil", "" ], [ "Lim", "JongYoon", "" ], [ "Ahn", "Ho Seok", "" ], [ "McGuinness", "Ben", "" ], [ "Downes", "Catherine", "" ], [ "Jangali", "Rahul", "" ], [ "Black", "Kale", "" ], [ "Lim", "Hin", "" ], [ "Duke", "Mike", "" ], [ "MacDonald", "Bruce", "" ], [ "Williams", "Henry", "" ] ]
new_dataset
0.999228
2308.07540
Andrew Zhu
Andrew Zhu and Lara J. Martin and Andrew Head and Chris Callison-Burch
CALYPSO: LLMs as Dungeon Masters' Assistants
11 pages, 4 figures. AIIDE 2023
null
null
null
cs.CL cs.HC
http://creativecommons.org/licenses/by/4.0/
The role of a Dungeon Master, or DM, in the game Dungeons & Dragons is to perform multiple tasks simultaneously. The DM must digest information about the game setting and monsters, synthesize scenes to present to other players, and respond to the players' interactions with the scene. Doing all of these tasks while maintaining consistency within the narrative and story world is no small feat of human cognition, making the task tiring and unapproachable to new players. Large language models (LLMs) like GPT-3 and ChatGPT have shown remarkable abilities to generate coherent natural language text. In this paper, we conduct a formative evaluation with DMs to establish the use cases of LLMs in D&D and tabletop gaming generally. We introduce CALYPSO, a system of LLM-powered interfaces that support DMs with information and inspiration specific to their own scenario. CALYPSO distills game context into bite-sized prose and helps brainstorm ideas without distracting the DM from the game. When given access to CALYPSO, DMs reported that it generated high-fidelity text suitable for direct presentation to players, and low-fidelity ideas that the DM could develop further while maintaining their creative agency. We see CALYPSO as exemplifying a paradigm of AI-augmented tools that provide synchronous creative assistance within established game worlds, and tabletop gaming more broadly.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 02:57:00 GMT" } ]
2023-08-16T00:00:00
[ [ "Zhu", "Andrew", "" ], [ "Martin", "Lara J.", "" ], [ "Head", "Andrew", "" ], [ "Callison-Burch", "Chris", "" ] ]
new_dataset
0.999081
2308.07571
Anbang Yao
Dongqi Cai, Yangyuxuan Kang, Anbang Yao, Yurong Chen
Ske2Grid: Skeleton-to-Grid Representation Learning for Action Recognition
The paper of Ske2Grid is published at ICML 2023. Code and models are available at https://github.com/OSVAI/Ske2Grid
null
null
null
cs.CV cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper presents Ske2Grid, a new representation learning framework for improved skeleton-based action recognition. In Ske2Grid, we define a regular convolution operation upon a novel grid representation of human skeleton, which is a compact image-like grid patch constructed and learned through three novel designs. Specifically, we propose a graph-node index transform (GIT) to construct a regular grid patch through assigning the nodes in the skeleton graph one by one to the desired grid cells. To ensure that GIT is a bijection and enrich the expressiveness of the grid representation, an up-sampling transform (UPT) is learned to interpolate the skeleton graph nodes for filling the grid patch to the full. To resolve the problem when the one-step UPT is aggressive and further exploit the representation capability of the grid patch with increasing spatial size, a progressive learning strategy (PLS) is proposed which decouples the UPT into multiple steps and aligns them to multiple paired GITs through a compact cascaded design learned progressively. We construct networks upon prevailing graph convolution networks and conduct experiments on six mainstream skeleton-based action recognition datasets. Experiments show that our Ske2Grid significantly outperforms existing GCN-based solutions under different benchmark settings, without bells and whistles. Code and models are available at https://github.com/OSVAI/Ske2Grid
[ { "version": "v1", "created": "Tue, 15 Aug 2023 04:49:11 GMT" } ]
2023-08-16T00:00:00
[ [ "Cai", "Dongqi", "" ], [ "Kang", "Yangyuxuan", "" ], [ "Yao", "Anbang", "" ], [ "Chen", "Yurong", "" ] ]
new_dataset
0.998516
2308.07580
Bo Lin
Bo Lin, Shoshanna Saxe, Timothy C. Y. Chan
AutoLTS: Automating Cycling Stress Assessment via Contrastive Learning and Spatial Post-processing
null
null
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by/4.0/
Cycling stress assessment, which quantifies cyclists' perceived stress imposed by the built environment and motor traffics, increasingly informs cycling infrastructure planning and cycling route recommendation. However, currently calculating cycling stress is slow and data-intensive, which hinders its broader application. In this paper, We propose a deep learning framework to support accurate, fast, and large-scale cycling stress assessments for urban road networks based on street-view images. Our framework features i) a contrastive learning approach that leverages the ordinal relationship among cycling stress labels, and ii) a post-processing technique that enforces spatial smoothness into our predictions. On a dataset of 39,153 road segments collected in Toronto, Canada, our results demonstrate the effectiveness of our deep learning framework and the value of using image data for cycling stress assessment in the absence of high-quality road geometry and motor traffic data.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 05:51:25 GMT" } ]
2023-08-16T00:00:00
[ [ "Lin", "Bo", "" ], [ "Saxe", "Shoshanna", "" ], [ "Chan", "Timothy C. Y.", "" ] ]
new_dataset
0.981348
2308.07593
JeongHun Yeo
Jeong Hun Yeo, Minsu Kim, Jeongsoo Choi, Dae Hoe Kim, and Yong Man Ro
AKVSR: Audio Knowledge Empowered Visual Speech Recognition by Compressing Audio Knowledge of a Pretrained Model
null
null
null
null
cs.CV cs.MM eess.AS eess.IV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Visual Speech Recognition (VSR) is the task of predicting spoken words from silent lip movements. VSR is regarded as a challenging task because of the insufficient information on lip movements. In this paper, we propose an Audio Knowledge empowered Visual Speech Recognition framework (AKVSR) to complement the insufficient speech information of visual modality by using audio modality. Different from the previous methods, the proposed AKVSR 1) utilizes rich audio knowledge encoded by a large-scale pretrained audio model, 2) saves the linguistic information of audio knowledge in compact audio memory by discarding the non-linguistic information from the audio through quantization, and 3) includes Audio Bridging Module which can find the best-matched audio features from the compact audio memory, which makes our training possible without audio inputs, once after the compact audio memory is composed. We validate the effectiveness of the proposed method through extensive experiments, and achieve new state-of-the-art performances on the widely-used datasets, LRS2 and LRS3.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 06:38:38 GMT" } ]
2023-08-16T00:00:00
[ [ "Yeo", "Jeong Hun", "" ], [ "Kim", "Minsu", "" ], [ "Choi", "Jeongsoo", "" ], [ "Kim", "Dae Hoe", "" ], [ "Ro", "Yong Man", "" ] ]
new_dataset
0.985627
2308.07605
Zhengwentai Sun
Zhengwentai Sun, Yanghong Zhou, Honghong He, P. Y. Mok
SGDiff: A Style Guided Diffusion Model for Fashion Synthesis
Accepted by ACM MM'23
null
10.1145/3581783.3613806
null
cs.CV cs.AI cs.MM
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper reports on the development of \textbf{a novel style guided diffusion model (SGDiff)} which overcomes certain weaknesses inherent in existing models for image synthesis. The proposed SGDiff combines image modality with a pretrained text-to-image diffusion model to facilitate creative fashion image synthesis. It addresses the limitations of text-to-image diffusion models by incorporating supplementary style guidance, substantially reducing training costs, and overcoming the difficulties of controlling synthesized styles with text-only inputs. This paper also introduces a new dataset -- SG-Fashion, specifically designed for fashion image synthesis applications, offering high-resolution images and an extensive range of garment categories. By means of comprehensive ablation study, we examine the application of classifier-free guidance to a variety of conditions and validate the effectiveness of the proposed model for generating fashion images of the desired categories, product attributes, and styles. The contributions of this paper include a novel classifier-free guidance method for multi-modal feature fusion, a comprehensive dataset for fashion image synthesis application, a thorough investigation on conditioned text-to-image synthesis, and valuable insights for future research in the text-to-image synthesis domain. The code and dataset are available at: \url{https://github.com/taited/SGDiff}.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 07:20:22 GMT" } ]
2023-08-16T00:00:00
[ [ "Sun", "Zhengwentai", "" ], [ "Zhou", "Yanghong", "" ], [ "He", "Honghong", "" ], [ "Mok", "P. Y.", "" ] ]
new_dataset
0.998852
2308.07622
Jialing Zou
Jialing Zou, Jiahao Mei, Guangze Ye, Tianyu Huai, Qiwei Shen, Daoguo Dong
EMID: An Emotional Aligned Dataset in Audio-Visual Modality
null
null
null
null
cs.MM
http://creativecommons.org/licenses/by-nc-sa/4.0/
In this paper, we propose Emotionally paired Music and Image Dataset (EMID), a novel dataset designed for the emotional matching of music and images, to facilitate auditory-visual cross-modal tasks such as generation and retrieval. Unlike existing approaches that primarily focus on semantic correlations or roughly divided emotional relations, EMID emphasizes the significance of emotional consistency between music and images using an advanced 13-dimension emotional model. By incorporating emotional alignment into the dataset, it aims to establish pairs that closely align with human perceptual understanding, thereby raising the performance of auditory-visual cross-modal tasks. We also design a supplemental module named EMI-Adapter to optimize existing cross-modal alignment methods. To validate the effectiveness of the EMID, we conduct a psychological experiment, which has demonstrated that considering the emotional relationship between the two modalities effectively improves the accuracy of matching in abstract perspective. This research lays the foundation for future cross-modal research in domains such as psychotherapy and contributes to advancing the understanding and utilization of emotions in cross-modal alignment. The EMID dataset is available at https://github.com/ecnu-aigc/EMID.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 08:13:14 GMT" } ]
2023-08-16T00:00:00
[ [ "Zou", "Jialing", "" ], [ "Mei", "Jiahao", "" ], [ "Ye", "Guangze", "" ], [ "Huai", "Tianyu", "" ], [ "Shen", "Qiwei", "" ], [ "Dong", "Daoguo", "" ] ]
new_dataset
0.999708
2308.07654
Jianyi Cheng
Jianyi Cheng, Samuel Coward, Lorenzo Chelini, Rafael Barbalho, Theo Drane
SEER: Super-Optimization Explorer for HLS using E-graph Rewriting with MLIR
null
null
null
null
cs.PL cs.AR cs.CL
http://creativecommons.org/licenses/by/4.0/
High-level synthesis (HLS) is a process that automatically translates a software program in a high-level language into a low-level hardware description. However, the hardware designs produced by HLS tools still suffer from a significant performance gap compared to manual implementations. This is because the input HLS programs must still be written using hardware design principles. Existing techniques either leave the program source unchanged or perform a fixed sequence of source transformation passes, potentially missing opportunities to find the optimal design. We propose a super-optimization approach for HLS that automatically rewrites an arbitrary software program into efficient HLS code that can be used to generate an optimized hardware design. We developed a toolflow named SEER, based on the e-graph data structure, to efficiently explore equivalent implementations of a program at scale. SEER provides an extensible framework, orchestrating existing software compiler passes and hardware synthesis optimizers. Our work is the first attempt to exploit e-graph rewriting for large software compiler frameworks, such as MLIR. Across a set of open-source benchmarks, we show that SEER achieves up to 38x the performance within 1.4x the area of the original program. Via an Intel-provided case study, SEER demonstrates the potential to outperform manually optimized designs produced by hardware experts.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 09:05:27 GMT" } ]
2023-08-16T00:00:00
[ [ "Cheng", "Jianyi", "" ], [ "Coward", "Samuel", "" ], [ "Chelini", "Lorenzo", "" ], [ "Barbalho", "Rafael", "" ], [ "Drane", "Theo", "" ] ]
new_dataset
0.997368
2308.07700
Serhii Nazarovets
Serhii Nazarovets, Olesya Mryglod
Ukrainian Arts and Humanities research in Scopus: A Bibliometric Analysis
Library Hi Tech (2023)
null
10.1108/LHT-05-2023-0180
null
cs.DL
http://creativecommons.org/licenses/by-nc-nd/4.0/
This article presents the results of a quantitative analysis of Ukrainian Arts and Humanities (A&H) research from 2012 to 2021, as observed in Scopus. The overall publication activity and the relative share of A&H publications in relation to Ukraine's total research output, comparing them with other countries. The study analyzes the diversity and total number of sources, as well as the geographic distribution of authors and citing authors, to provide insights into the internationalization level of Ukrainian A&H research. Additionally, the topical spectrum and language usage are considered to complete the overall picture. According to our results, the publication patterns for Ukrainian A&H research exhibit dynamics comparable to those of other countries, with a gradual increase in the total number of papers and sources. However, the citedness is lower than expected, and the share of publications in top-quartile sources is lower for 2020-2021 period compared to the previous years. The impact of internationally collaborative papers, especially those in English, is higher. Nevertheless, over half of all works remain uncited, probably due to the limited readership of the journals selected for publication.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 11:05:04 GMT" } ]
2023-08-16T00:00:00
[ [ "Nazarovets", "Serhii", "" ], [ "Mryglod", "Olesya", "" ] ]
new_dataset
0.996917
2308.07717
Ching-Hsun Tseng
Ching-Hsun Tseng, Shao-Ju Chien, Po-Shen Wang, Shin-Jye Lee, Wei-Huan Hu, Bin Pu, and Xiao-jun Zeng
Real-time Automatic M-mode Echocardiography Measurement with Panel Attention from Local-to-Global Pixels
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Motion mode (M-mode) recording is an essential part of echocardiography to measure cardiac dimension and function. However, the current diagnosis cannot build an automatic scheme, as there are three fundamental obstructs: Firstly, there is no open dataset available to build the automation for ensuring constant results and bridging M-mode echocardiography with real-time instance segmentation (RIS); Secondly, the examination is involving the time-consuming manual labelling upon M-mode echocardiograms; Thirdly, as objects in echocardiograms occupy a significant portion of pixels, the limited receptive field in existing backbones (e.g., ResNet) composed from multiple convolution layers are inefficient to cover the period of a valve movement. Existing non-local attentions (NL) compromise being unable real-time with a high computation overhead or losing information from a simplified version of the non-local block. Therefore, we proposed RAMEM, a real-time automatic M-mode echocardiography measurement scheme, contributes three aspects to answer the problems: 1) provide MEIS, a dataset of M-mode echocardiograms for instance segmentation, to enable consistent results and support the development of an automatic scheme; 2) propose panel attention, local-to-global efficient attention by pixel-unshuffling, embedding with updated UPANets V2 in a RIS scheme toward big object detection with global receptive field; 3) develop and implement AMEM, an efficient algorithm of automatic M-mode echocardiography measurement enabling fast and accurate automatic labelling among diagnosis. The experimental results show that RAMEM surpasses existing RIS backbones (with non-local attention) in PASCAL 2012 SBD and human performances in real-time MEIS tested. The code of MEIS and dataset are available at https://github.com/hanktseng131415go/RAME.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 11:50:57 GMT" } ]
2023-08-16T00:00:00
[ [ "Tseng", "Ching-Hsun", "" ], [ "Chien", "Shao-Ju", "" ], [ "Wang", "Po-Shen", "" ], [ "Lee", "Shin-Jye", "" ], [ "Hu", "Wei-Huan", "" ], [ "Pu", "Bin", "" ], [ "Zeng", "Xiao-jun", "" ] ]
new_dataset
0.999023
2308.07732
Haiyang Wang
Haiyang Wang, Hao Tang, Shaoshuai Shi, Aoxue Li, Zhenguo Li, Bernt Schiele, Liwei Wang
UniTR: A Unified and Efficient Multi-Modal Transformer for Bird's-Eye-View Representation
Accepted by ICCV2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Jointly processing information from multiple sensors is crucial to achieving accurate and robust perception for reliable autonomous driving systems. However, current 3D perception research follows a modality-specific paradigm, leading to additional computation overheads and inefficient collaboration between different sensor data. In this paper, we present an efficient multi-modal backbone for outdoor 3D perception named UniTR, which processes a variety of modalities with unified modeling and shared parameters. Unlike previous works, UniTR introduces a modality-agnostic transformer encoder to handle these view-discrepant sensor data for parallel modal-wise representation learning and automatic cross-modal interaction without additional fusion steps. More importantly, to make full use of these complementary sensor types, we present a novel multi-modal integration strategy by both considering semantic-abundant 2D perspective and geometry-aware 3D sparse neighborhood relations. UniTR is also a fundamentally task-agnostic backbone that naturally supports different 3D perception tasks. It sets a new state-of-the-art performance on the nuScenes benchmark, achieving +1.1 NDS higher for 3D object detection and +12.0 higher mIoU for BEV map segmentation with lower inference latency. Code will be available at https://github.com/Haiyang-W/UniTR .
[ { "version": "v1", "created": "Tue, 15 Aug 2023 12:13:44 GMT" } ]
2023-08-16T00:00:00
[ [ "Wang", "Haiyang", "" ], [ "Tang", "Hao", "" ], [ "Shi", "Shaoshuai", "" ], [ "Li", "Aoxue", "" ], [ "Li", "Zhenguo", "" ], [ "Schiele", "Bernt", "" ], [ "Wang", "Liwei", "" ] ]
new_dataset
0.996019
2308.07743
Wenyuan Xue
Wenyuan Xue, Dapeng Chen, Baosheng Yu, Yifei Chen, Sai Zhou, Wei Peng
ChartDETR: A Multi-shape Detection Network for Visual Chart Recognition
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Visual chart recognition systems are gaining increasing attention due to the growing demand for automatically identifying table headers and values from chart images. Current methods rely on keypoint detection to estimate data element shapes in charts but suffer from grouping errors in post-processing. To address this issue, we propose ChartDETR, a transformer-based multi-shape detector that localizes keypoints at the corners of regular shapes to reconstruct multiple data elements in a single chart image. Our method predicts all data element shapes at once by introducing query groups in set prediction, eliminating the need for further postprocessing. This property allows ChartDETR to serve as a unified framework capable of representing various chart types without altering the network architecture, effectively detecting data elements of diverse shapes. We evaluated ChartDETR on three datasets, achieving competitive results across all chart types without any additional enhancements. For example, ChartDETR achieved an F1 score of 0.98 on Adobe Synthetic, significantly outperforming the previous best model with a 0.71 F1 score. Additionally, we obtained a new state-of-the-art result of 0.97 on ExcelChart400k. The code will be made publicly available.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 12:50:06 GMT" } ]
2023-08-16T00:00:00
[ [ "Xue", "Wenyuan", "" ], [ "Chen", "Dapeng", "" ], [ "Yu", "Baosheng", "" ], [ "Chen", "Yifei", "" ], [ "Zhou", "Sai", "" ], [ "Peng", "Wei", "" ] ]
new_dataset
0.999501
2308.07749
Bosheng Qin
Bosheng Qin, Wentao Ye, Qifan Yu, Siliang Tang, Yueting Zhuang
Dancing Avatar: Pose and Text-Guided Human Motion Videos Synthesis with Image Diffusion Model
11 pages, 3 figures
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The rising demand for creating lifelike avatars in the digital realm has led to an increased need for generating high-quality human videos guided by textual descriptions and poses. We propose Dancing Avatar, designed to fabricate human motion videos driven by poses and textual cues. Our approach employs a pretrained T2I diffusion model to generate each video frame in an autoregressive fashion. The crux of innovation lies in our adept utilization of the T2I diffusion model for producing video frames successively while preserving contextual relevance. We surmount the hurdles posed by maintaining human character and clothing consistency across varying poses, along with upholding the background's continuity amidst diverse human movements. To ensure consistent human appearances across the entire video, we devise an intra-frame alignment module. This module assimilates text-guided synthesized human character knowledge into the pretrained T2I diffusion model, synergizing insights from ChatGPT. For preserving background continuity, we put forth a background alignment pipeline, amalgamating insights from segment anything and image inpainting techniques. Furthermore, we propose an inter-frame alignment module that draws inspiration from an auto-regressive pipeline to augment temporal consistency between adjacent frames, where the preceding frame guides the synthesis process of the current frame. Comparisons with state-of-the-art methods demonstrate that Dancing Avatar exhibits the capacity to generate human videos with markedly superior quality, both in terms of human and background fidelity, as well as temporal coherence compared to existing state-of-the-art approaches.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 13:00:42 GMT" } ]
2023-08-16T00:00:00
[ [ "Qin", "Bosheng", "" ], [ "Ye", "Wentao", "" ], [ "Yu", "Qifan", "" ], [ "Tang", "Siliang", "" ], [ "Zhuang", "Yueting", "" ] ]
new_dataset
0.999211
2308.07771
Wei Qian
Wei Qian, Dan Guo, Kun Li, Xilan Tian, Meng Wang
Dual-path TokenLearner for Remote Photoplethysmography-based Physiological Measurement with Facial Videos
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Remote photoplethysmography (rPPG) based physiological measurement is an emerging yet crucial vision task, whose challenge lies in exploring accurate rPPG prediction from facial videos accompanied by noises of illumination variations, facial occlusions, head movements, \etc, in a non-contact manner. Existing mainstream CNN-based models make efforts to detect physiological signals by capturing subtle color changes in facial regions of interest (ROI) caused by heartbeats. However, such models are constrained by the limited local spatial or temporal receptive fields in the neural units. Unlike them, a native Transformer-based framework called Dual-path TokenLearner (Dual-TL) is proposed in this paper, which utilizes the concept of learnable tokens to integrate both spatial and temporal informative contexts from the global perspective of the video. Specifically, the proposed Dual-TL uses a Spatial TokenLearner (S-TL) to explore associations in different facial ROIs, which promises the rPPG prediction far away from noisy ROI disturbances. Complementarily, a Temporal TokenLearner (T-TL) is designed to infer the quasi-periodic pattern of heartbeats, which eliminates temporal disturbances such as head movements. The two TokenLearners, S-TL and T-TL, are executed in a dual-path mode. This enables the model to reduce noise disturbances for final rPPG signal prediction. Extensive experiments on four physiological measurement benchmark datasets are conducted. The Dual-TL achieves state-of-the-art performances in both intra- and cross-dataset testings, demonstrating its immense potential as a basic backbone for rPPG measurement. The source code is available at \href{https://github.com/VUT-HFUT/Dual-TL}{https://github.com/VUT-HFUT/Dual-TL}
[ { "version": "v1", "created": "Tue, 15 Aug 2023 13:45:45 GMT" } ]
2023-08-16T00:00:00
[ [ "Qian", "Wei", "" ], [ "Guo", "Dan", "" ], [ "Li", "Kun", "" ], [ "Tian", "Xilan", "" ], [ "Wang", "Meng", "" ] ]
new_dataset
0.998768
2308.07799
Raphaela Heil
Raphaela Heil, Malin Nauwerck
Handwritten Stenography Recognition and the LION Dataset
null
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Purpose: In this paper, we establish a baseline for handwritten stenography recognition, using the novel LION dataset, and investigate the impact of including selected aspects of stenographic theory into the recognition process. We make the LION dataset publicly available with the aim of encouraging future research in handwritten stenography recognition. Methods: A state-of-the-art text recognition model is trained to establish a baseline. Stenographic domain knowledge is integrated by applying four different encoding methods that transform the target sequence into representations, which approximate selected aspects of the writing system. Results are further improved by integrating a pre-training scheme, based on synthetic data. Results: The baseline model achieves an average test character error rate (CER) of 29.81% and a word error rate (WER) of 55.14%. Test error rates are reduced significantly by combining stenography-specific target sequence encodings with pre-training and fine-tuning, yielding CERs in the range of 24.5% - 26% and WERs of 44.8% - 48.2%. Conclusion: The obtained results demonstrate the challenging nature of stenography recognition. Integrating stenography-specific knowledge, in conjunction with pre-training and fine-tuning on synthetic data, yields considerable improvements. Together with our precursor study on the subject, this is the first work to apply modern handwritten text recognition to stenography. The dataset and our code are publicly available via Zenodo.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 14:25:53 GMT" } ]
2023-08-16T00:00:00
[ [ "Heil", "Raphaela", "" ], [ "Nauwerck", "Malin", "" ] ]
new_dataset
0.999797
2308.07802
Paul Kielty
Paul Kielty, Cian Ryan, Mehdi Sefidgar Dilmaghani, Waseem Shariff, Joe Lemley, Peter Corcoran
Neuromorphic Seatbelt State Detection for In-Cabin Monitoring with Event Cameras
4 pages, 3 figures, IMVIP 2023
Zenodo (2023)
10.5281/zenodo.8223905
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Neuromorphic vision sensors, or event cameras, differ from conventional cameras in that they do not capture images at a specified rate. Instead, they asynchronously log local brightness changes at each pixel. As a result, event cameras only record changes in a given scene, and do so with very high temporal resolution, high dynamic range, and low power requirements. Recent research has demonstrated how these characteristics make event cameras extremely practical sensors in driver monitoring systems (DMS), enabling the tracking of high-speed eye motion and blinks. This research provides a proof of concept to expand event-based DMS techniques to include seatbelt state detection. Using an event simulator, a dataset of 108,691 synthetic neuromorphic frames of car occupants was generated from a near-infrared (NIR) dataset, and split into training, validation, and test sets for a seatbelt state detection algorithm based on a recurrent convolutional neural network (CNN). In addition, a smaller set of real event data was collected and reserved for testing. In a binary classification task, the fastened/unfastened frames were identified with an F1 score of 0.989 and 0.944 on the simulated and real test sets respectively. When the problem extended to also classify the action of fastening/unfastening the seatbelt, respective F1 scores of 0.964 and 0.846 were achieved.
[ { "version": "v1", "created": "Tue, 15 Aug 2023 14:27:46 GMT" } ]
2023-08-16T00:00:00
[ [ "Kielty", "Paul", "" ], [ "Ryan", "Cian", "" ], [ "Dilmaghani", "Mehdi Sefidgar", "" ], [ "Shariff", "Waseem", "" ], [ "Lemley", "Joe", "" ], [ "Corcoran", "Peter", "" ] ]
new_dataset
0.999189
1408.0366
Yoshihiro Terasawa
Yoshihiro Terasawa
Publickey encryption by ordering
I want to rewrite
null
null
null
cs.CR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In 1999, public key cryptography using the matrix was devised by a hish school student of 16 yesrs old girl Sarah Flannery. This cryptosystem seemed faster than RSA, and it's having the strength to surpass even the encryption to RSA. However, this encryption scheme was broken bfore har papers were published. In this paper, We try to construct publickey encryption scheme from permutation group that is equivalent to matrix as noncommutative group. And we explore the potential of this cryptsystem through implementation.
[ { "version": "v1", "created": "Sat, 2 Aug 2014 12:49:40 GMT" }, { "version": "v2", "created": "Sun, 13 Aug 2023 11:46:31 GMT" } ]
2023-08-15T00:00:00
[ [ "Terasawa", "Yoshihiro", "" ] ]
new_dataset
0.987796
2011.09896
Nikolaus Piccolotto
Nikolaus Piccolotto, Markus B\"ogl, Theresia Gschwandtner, Christoph Muehlmann, Klaus Nordhausen, Peter Filzmoser and Silvia Miksch
TBSSvis: Visual Analytics for Temporal Blind Source Separation
null
Visual Informatics, 6, 51-66, 2022
10.1016/j.visinf.2022.10.002
null
cs.HC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Temporal Blind Source Separation (TBSS) is used to obtain the true underlying processes from noisy temporal multivariate data, such as electrocardiograms. TBSS has similarities to Principal Component Analysis (PCA) as it separates the input data into univariate components and is applicable to suitable datasets from various domains, such as medicine, finance, or civil engineering. Despite TBSS's broad applicability, the involved tasks are not well supported in current tools, which offer only text-based interactions and single static images. Analysts are limited in analyzing and comparing obtained results, which consist of diverse data such as matrices and sets of time series. Additionally, parameter settings have a big impact on separation performance, but as a consequence of improper tooling, analysts currently do not consider the whole parameter space. We propose to solve these problems by applying visual analytics (VA) principles. Our primary contribution is a design study for TBSS, which so far has not been explored by the visualization community. We developed a task abstraction and visualization design in a user-centered design process. Task-specific assembling of well-established visualization techniques and algorithms to gain insights in the TBSS processes is our secondary contribution. We present TBSSvis, an interactive web-based VA prototype, which we evaluated extensively in two interviews with five TBSS experts. Feedback and observations from these interviews show that TBSSvis supports the actual workflow and combination of interactive visualizations that facilitate the tasks involved in analyzing TBSS results.
[ { "version": "v1", "created": "Thu, 19 Nov 2020 15:29:16 GMT" }, { "version": "v2", "created": "Wed, 23 Feb 2022 10:27:49 GMT" } ]
2023-08-15T00:00:00
[ [ "Piccolotto", "Nikolaus", "" ], [ "Bögl", "Markus", "" ], [ "Gschwandtner", "Theresia", "" ], [ "Muehlmann", "Christoph", "" ], [ "Nordhausen", "Klaus", "" ], [ "Filzmoser", "Peter", "" ], [ "Miksch", "Silvia", "" ] ]
new_dataset
0.99866
2112.06300
Zachary Ferguson
David Belgrod, Bolun Wang, Zachary Ferguson, Xin Zhao, Marco Attene, Daniele Panozzo, Teseo Schneider
Time of Impact Dataset for Continuous Collision Detection and a Scalable Conservative Algorithm
null
null
null
null
cs.GR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We introduce a large-scale benchmark for broad- and narrow-phase continuous collision detection (CCD) over linearized trajectories with exact time of impacts and use it to evaluate the accuracy, correctness, and efficiency of 13 state-of-the-art CCD algorithms. Our analysis shows that several methods exhibit problems either in efficiency or accuracy. To overcome these limitations, we introduce an algorithm for CCD designed to be scalable on modern parallel architectures and provably correct when implemented using floating point arithmetic. We integrate our algorithm within the Incremental Potential Contact solver [Li et al . 2021] and evaluate its impact on various simulation scenarios. Our approach includes a broad-phase CCD to quickly filter out primitives having disjoint bounding boxes and a narrow-phase CCD that establishes whether the remaining primitive pairs indeed collide. Our broad-phase algorithm is efficient and scalable thanks to the experimental observation that sweeping along a coordinate axis performs surprisingly well on modern parallel architectures. For narrow-phase CCD, we re-design the recently proposed interval-based algorithm of Wang et al. [2021] to work on massively parallel hardware. To foster the adoption and development of future linear CCD algorithms, and to evaluate their correctness, scalability, and overall performance, we release the dataset with analytic ground truth, the implementation of all the algorithms tested, and our testing framework.
[ { "version": "v1", "created": "Sun, 12 Dec 2021 18:47:55 GMT" }, { "version": "v2", "created": "Tue, 1 Feb 2022 00:45:48 GMT" }, { "version": "v3", "created": "Mon, 22 Aug 2022 21:56:18 GMT" }, { "version": "v4", "created": "Sun, 13 Aug 2023 08:02:00 GMT" } ]
2023-08-15T00:00:00
[ [ "Belgrod", "David", "" ], [ "Wang", "Bolun", "" ], [ "Ferguson", "Zachary", "" ], [ "Zhao", "Xin", "" ], [ "Attene", "Marco", "" ], [ "Panozzo", "Daniele", "" ], [ "Schneider", "Teseo", "" ] ]
new_dataset
0.998673
2204.09803
Jintang Li
Jintang Li, Jie Liao, Ruofan Wu, Liang Chen, Zibin Zheng, Jiawang Dan, Changhua Meng, Weiqiang Wang
GUARD: Graph Universal Adversarial Defense
Accepted by CIKM 2023. Code is publicly available at https://github.com/EdisonLeeeee/GUARD
null
null
null
cs.LG cs.AI cs.CR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Graph convolutional networks (GCNs) have been shown to be vulnerable to small adversarial perturbations, which becomes a severe threat and largely limits their applications in security-critical scenarios. To mitigate such a threat, considerable research efforts have been devoted to increasing the robustness of GCNs against adversarial attacks. However, current defense approaches are typically designed to prevent GCNs from untargeted adversarial attacks and focus on overall performance, making it challenging to protect important local nodes from more powerful targeted adversarial attacks. Additionally, a trade-off between robustness and performance is often made in existing research. Such limitations highlight the need for developing an effective and efficient approach that can defend local nodes against targeted attacks, without compromising the overall performance of GCNs. In this work, we present a simple yet effective method, named Graph Universal Adversarial Defense (GUARD). Unlike previous works, GUARD protects each individual node from attacks with a universal defensive patch, which is generated once and can be applied to any node (node-agnostic) in a graph. GUARD is fast, straightforward to implement without any change to network architecture nor any additional parameters, and is broadly applicable to any GCNs. Extensive experiments on four benchmark datasets demonstrate that GUARD significantly improves robustness for several established GCNs against multiple adversarial attacks and outperforms state-of-the-art defense methods by large margins.
[ { "version": "v1", "created": "Wed, 20 Apr 2022 22:18:12 GMT" }, { "version": "v2", "created": "Thu, 19 May 2022 09:49:34 GMT" }, { "version": "v3", "created": "Mon, 15 Aug 2022 09:10:01 GMT" }, { "version": "v4", "created": "Sat, 12 Aug 2023 10:03:40 GMT" } ]
2023-08-15T00:00:00
[ [ "Li", "Jintang", "" ], [ "Liao", "Jie", "" ], [ "Wu", "Ruofan", "" ], [ "Chen", "Liang", "" ], [ "Zheng", "Zibin", "" ], [ "Dan", "Jiawang", "" ], [ "Meng", "Changhua", "" ], [ "Wang", "Weiqiang", "" ] ]
new_dataset
0.959913
2206.04596
Sarvesh Bipin Patil
Sarvesh Patil, Tony Tao, Tess Hellebrekers, Oliver Kroemer, F. Zeynep Temel
Linear Delta Arrays for Compliant Dexterous Distributed Manipulation
ICRA 2023
null
10.1109/ICRA48891.2023.10160578
null
cs.RO cs.MA cs.SY eess.SY
http://creativecommons.org/licenses/by/4.0/
This paper presents a new type of distributed dexterous manipulator: delta arrays. Our delta array setup consists of 64 linearly-actuated delta robots with 3D-printed compliant linkages. Through the design of the individual delta robots, the modular array structure, and distributed communication and control, we study a wide range of in-plane and out-of-plane manipulations, as well as prehensile manipulations among subsets of neighboring delta robots. We also demonstrate dexterous manipulation capabilities of the delta array using reinforcement learning while leveraging the compliance to not break the end-effectors. Our evaluations show that the resulting 192 DoF compliant robot is capable of performing various coordinated distributed manipulations of a variety of objects, including translation, alignment, prehensile squeezing, lifting, and grasping.
[ { "version": "v1", "created": "Thu, 9 Jun 2022 16:23:42 GMT" }, { "version": "v2", "created": "Wed, 28 Sep 2022 22:53:26 GMT" }, { "version": "v3", "created": "Mon, 14 Aug 2023 11:52:57 GMT" } ]
2023-08-15T00:00:00
[ [ "Patil", "Sarvesh", "" ], [ "Tao", "Tony", "" ], [ "Hellebrekers", "Tess", "" ], [ "Kroemer", "Oliver", "" ], [ "Temel", "F. Zeynep", "" ] ]
new_dataset
0.99766
2207.00721
Sarvesh Bipin Patil
Sarvesh Patil, Samuel C. Alvares, Pragna Mannam, Oliver Kroemer, F. Zeynep Temel
DeltaZ: An Accessible Compliant Delta Robot Manipulator for Research and Education
IROS 2022, first two authors contributed equally
IEEE International Conference on Robotics and Automation (ICRA), 2023, 10324-10330
10.1109/IROS47612.2022.9981257
null
cs.RO cs.SY eess.SY
http://creativecommons.org/licenses/by/4.0/
This paper presents the DeltaZ robot, a centimeter-scale, low-cost, delta-style robot that allows for a broad range of capabilities and robust functionalities. Current technologies allow DeltaZ to be 3D-printed from soft and rigid materials so that it is easy to assemble and maintain, and lowers the barriers to utilize. Functionality of the robot stems from its three translational degrees of freedom and a closed form kinematic solution which makes manipulation problems more intuitive compared to other manipulators. Moreover, the low cost of the robot presents an opportunity to democratize manipulators for a research setting. We also describe how the robot can be used as a reinforcement learning benchmark. Open-source 3D-printable designs and code are available to the public.
[ { "version": "v1", "created": "Sat, 2 Jul 2022 03:01:03 GMT" } ]
2023-08-15T00:00:00
[ [ "Patil", "Sarvesh", "" ], [ "Alvares", "Samuel C.", "" ], [ "Mannam", "Pragna", "" ], [ "Kroemer", "Oliver", "" ], [ "Temel", "F. Zeynep", "" ] ]
new_dataset
0.999547
2208.00847
Wei Dai
Yuanyuan Liu, Wei Dai, Chuanxu Feng, Wenbin Wang, Guanghao Yin, Jiabei Zeng and Shiguang Shan
MAFW: A Large-scale, Multi-modal, Compound Affective Database for Dynamic Facial Expression Recognition in the Wild
This paper has been accepted by ACM MM'22
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Dynamic facial expression recognition (FER) databases provide important data support for affective computing and applications. However, most FER databases are annotated with several basic mutually exclusive emotional categories and contain only one modality, e.g., videos. The monotonous labels and modality cannot accurately imitate human emotions and fulfill applications in the real world. In this paper, we propose MAFW, a large-scale multi-modal compound affective database with 10,045 video-audio clips in the wild. Each clip is annotated with a compound emotional category and a couple of sentences that describe the subjects' affective behaviors in the clip. For the compound emotion annotation, each clip is categorized into one or more of the 11 widely-used emotions, i.e., anger, disgust, fear, happiness, neutral, sadness, surprise, contempt, anxiety, helplessness, and disappointment. To ensure high quality of the labels, we filter out the unreliable annotations by an Expectation Maximization (EM) algorithm, and then obtain 11 single-label emotion categories and 32 multi-label emotion categories. To the best of our knowledge, MAFW is the first in-the-wild multi-modal database annotated with compound emotion annotations and emotion-related captions. Additionally, we also propose a novel Transformer-based expression snippet feature learning method to recognize the compound emotions leveraging the expression-change relations among different emotions and modalities. Extensive experiments on MAFW database show the advantages of the proposed method over other state-of-the-art methods for both uni- and multi-modal FER. Our MAFW database is publicly available from https://mafw-database.github.io/MAFW.
[ { "version": "v1", "created": "Mon, 1 Aug 2022 13:34:33 GMT" }, { "version": "v2", "created": "Mon, 14 Aug 2023 05:22:41 GMT" } ]
2023-08-15T00:00:00
[ [ "Liu", "Yuanyuan", "" ], [ "Dai", "Wei", "" ], [ "Feng", "Chuanxu", "" ], [ "Wang", "Wenbin", "" ], [ "Yin", "Guanghao", "" ], [ "Zeng", "Jiabei", "" ], [ "Shan", "Shiguang", "" ] ]
new_dataset
0.999705
2210.06551
Wentao Zhu
Wentao Zhu, Xiaoxuan Ma, Zhaoyang Liu, Libin Liu, Wayne Wu, Yizhou Wang
MotionBERT: A Unified Perspective on Learning Human Motion Representations
ICCV 2023 Camera Ready
null
null
null
cs.CV
http://creativecommons.org/licenses/by-sa/4.0/
We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources. Specifically, we propose a pretraining stage in which a motion encoder is trained to recover the underlying 3D motion from noisy partial 2D observations. The motion representations acquired in this way incorporate geometric, kinematic, and physical knowledge about human motion, which can be easily transferred to multiple downstream tasks. We implement the motion encoder with a Dual-stream Spatio-temporal Transformer (DSTformer) neural network. It could capture long-range spatio-temporal relationships among the skeletal joints comprehensively and adaptively, exemplified by the lowest 3D pose estimation error so far when trained from scratch. Furthermore, our proposed framework achieves state-of-the-art performance on all three downstream tasks by simply finetuning the pretrained motion encoder with a simple regression head (1-2 layers), which demonstrates the versatility of the learned motion representations. Code and models are available at https://motionbert.github.io/
[ { "version": "v1", "created": "Wed, 12 Oct 2022 19:46:25 GMT" }, { "version": "v2", "created": "Wed, 22 Mar 2023 06:34:14 GMT" }, { "version": "v3", "created": "Wed, 19 Jul 2023 08:54:27 GMT" }, { "version": "v4", "created": "Thu, 20 Jul 2023 04:59:45 GMT" }, { "version": "v5", "created": "Mon, 14 Aug 2023 12:11:35 GMT" } ]
2023-08-15T00:00:00
[ [ "Zhu", "Wentao", "" ], [ "Ma", "Xiaoxuan", "" ], [ "Liu", "Zhaoyang", "" ], [ "Liu", "Libin", "" ], [ "Wu", "Wayne", "" ], [ "Wang", "Yizhou", "" ] ]
new_dataset
0.999485
2212.10963
Simon Erfurth
Joan Boyar, Simon Erfurth, Kim S. Larsen, Ruben Niederhagen
Quotable Signatures for Authenticating Shared Quotes
25 pages, 7 figures
null
null
null
cs.CR cs.CY
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Quotable signature schemes are digital signature schemes with the additional property that from the signature for a message, any party can extract signatures for (allowable) quotes from the message, without knowing the secret key or interacting with the signer of the original message. Crucially, the extracted signatures are still signed with the original secret key. We define a notion of security for quotable signature schemes and construct a concrete example of a quotable signature scheme, using Merkle trees and classical digital signature schemes. The scheme is shown to be secure, with respect to the aforementioned notion of security. Additionally, we prove bounds on the complexity of the constructed scheme and provide algorithms for signing, quoting, and verifying. Finally, concrete use cases of quotable signatures are considered, using them to combat misinformation by bolstering authentic content on social media. We consider both how quotable signatures can be used, and why using them could help mitigate the effects of fake news.
[ { "version": "v1", "created": "Wed, 21 Dec 2022 12:07:46 GMT" }, { "version": "v2", "created": "Fri, 10 Mar 2023 04:55:26 GMT" }, { "version": "v3", "created": "Fri, 21 Jul 2023 12:58:41 GMT" }, { "version": "v4", "created": "Mon, 14 Aug 2023 09:26:21 GMT" } ]
2023-08-15T00:00:00
[ [ "Boyar", "Joan", "" ], [ "Erfurth", "Simon", "" ], [ "Larsen", "Kim S.", "" ], [ "Niederhagen", "Ruben", "" ] ]
new_dataset
0.958021
2301.00626
Alejandro Vigna-Gomez
Alejandro Vigna-G\'omez, Javier Murillo, Manelik Ramirez, Alberto Borbolla, Ian M\'arquez and Prasun K. Ray
Design and analysis of tweet-based election models for the 2021 Mexican legislative election
Accepted for publication in EPJ Data Science. 20 pages, 7 figures, 1 table
null
10.1140/epjds/s13688-023-00401-w
null
cs.SI cs.CL cs.CY
http://creativecommons.org/licenses/by/4.0/
Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.
[ { "version": "v1", "created": "Mon, 2 Jan 2023 12:40:05 GMT" }, { "version": "v2", "created": "Wed, 21 Jun 2023 08:01:38 GMT" } ]
2023-08-15T00:00:00
[ [ "Vigna-Gómez", "Alejandro", "" ], [ "Murillo", "Javier", "" ], [ "Ramirez", "Manelik", "" ], [ "Borbolla", "Alberto", "" ], [ "Márquez", "Ian", "" ], [ "Ray", "Prasun K.", "" ] ]
new_dataset
0.999063
2301.06719
Yh.Peng Tu
Peng Tu, Xu Xie, Guo AI, Yuexiang Li, Yawen Huang, Yefeng Zheng
FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs
ICCV 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Efficient detectors for edge devices are often optimized for parameters or speed count metrics, which remain in weak correlation with the energy of detectors. However, some vision applications of convolutional neural networks, such as always-on surveillance cameras, are critical for energy constraints. This paper aims to serve as a baseline by designing detectors to reach tradeoffs between energy and performance from two perspectives: 1) We extensively analyze various CNNs to identify low-energy architectures, including selecting activation functions, convolutions operators, and feature fusion structures on necks. These underappreciated details in past work seriously affect the energy consumption of detectors; 2) To break through the dilemmatic energy-performance problem, we propose a balanced detector driven by energy using discovered low-energy components named \textit{FemtoDet}. In addition to the novel construction, we improve FemtoDet by considering convolutions and training strategy optimizations. Specifically, we develop a new instance boundary enhancement (IBE) module for convolution optimization to overcome the contradiction between the limited capacity of CNNs and detection tasks in diverse spatial representations, and propose a recursive warm-restart (RecWR) for optimizing training strategy to escape the sub-optimization of light-weight detectors by considering the data shift produced in popular augmentations. As a result, FemtoDet with only 68.77k parameters achieves a competitive score of 46.3 AP50 on PASCAL VOC and 1.11 W $\&$ 64.47 FPS on Qualcomm Snapdragon 865 CPU platforms. Extensive experiments on COCO and TJU-DHD datasets indicate that the proposed method achieves competitive results in diverse scenes.
[ { "version": "v1", "created": "Tue, 17 Jan 2023 06:24:08 GMT" }, { "version": "v2", "created": "Thu, 25 May 2023 15:57:28 GMT" }, { "version": "v3", "created": "Fri, 14 Jul 2023 07:36:01 GMT" }, { "version": "v4", "created": "Mon, 17 Jul 2023 02:40:42 GMT" }, { "version": "v5", "created": "Sun, 13 Aug 2023 17:25:45 GMT" } ]
2023-08-15T00:00:00
[ [ "Tu", "Peng", "" ], [ "Xie", "Xu", "" ], [ "AI", "Guo", "" ], [ "Li", "Yuexiang", "" ], [ "Huang", "Yawen", "" ], [ "Zheng", "Yefeng", "" ] ]
new_dataset
0.994735
2303.00277
Sier Ha
Ha Sier, Xianjia Yu, Iacopo Catalano, Jorge Pena Queralta, Zhuo Zou and Tomi Westerlund
UAV Tracking with Lidar as a Camera Sensors in GNSS-Denied Environments
I need to make some revisions to the paper because there are some mistakes in the paper
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
LiDAR has become one of the primary sensors in robotics and autonomous system for high-accuracy situational awareness. In recent years, multi-modal LiDAR systems emerged, and among them, LiDAR-as-a-camera sensors provide not only 3D point clouds but also fixed-resolution 360{\deg}panoramic images by encoding either depth, reflectivity, or near-infrared light in the image pixels. This potentially brings computer vision capabilities on top of the potential of LiDAR itself. In this paper, we are specifically interested in utilizing LiDARs and LiDAR-generated images for tracking Unmanned Aerial Vehicles (UAVs) in real-time which can benefit applications including docking, remote identification, or counter-UAV systems, among others. This is, to the best of our knowledge, the first work that explores the possibility of fusing the images and point cloud generated by a single LiDAR sensor to track a UAV without a priori known initialized position. We trained a custom YOLOv5 model for detecting UAVs based on the panoramic images collected in an indoor experiment arena with a MOCAP system. By integrating with the point cloud, we are able to continuously provide the position of the UAV. Our experiment demonstrated the effectiveness of the proposed UAV tracking approach compared with methods based only on point clouds or images. Additionally, we evaluated the real-time performance of our approach on the Nvidia Jetson Nano, a popular mobile computing platform.
[ { "version": "v1", "created": "Wed, 1 Mar 2023 06:55:49 GMT" }, { "version": "v2", "created": "Tue, 11 Apr 2023 11:40:11 GMT" }, { "version": "v3", "created": "Mon, 14 Aug 2023 11:04:31 GMT" } ]
2023-08-15T00:00:00
[ [ "Sier", "Ha", "" ], [ "Yu", "Xianjia", "" ], [ "Catalano", "Iacopo", "" ], [ "Queralta", "Jorge Pena", "" ], [ "Zou", "Zhuo", "" ], [ "Westerlund", "Tomi", "" ] ]
new_dataset
0.999439
2303.01664
Yuma Koizumi
Yuma Koizumi, Heiga Zen, Shigeki Karita, Yifan Ding, Kohei Yatabe, Nobuyuki Morioka, Yu Zhang, Wei Han, Ankur Bapna, Michiel Bacchiani
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations
Accepted to WASPAA 2023
null
null
null
cs.SD cs.LG eess.AS
http://creativecommons.org/licenses/by-nc-nd/4.0/
Speech restoration (SR) is a task of converting degraded speech signals into high-quality ones. In this study, we propose a robust SR model called Miipher, and apply Miipher to a new SR application: increasing the amount of high-quality training data for speech generation by converting speech samples collected from the Web to studio-quality. To make our SR model robust against various degradation, we use (i) a speech representation extracted from w2v-BERT for the input feature, and (ii) a text representation extracted from transcripts via PnG-BERT as a linguistic conditioning feature. Experiments show that Miipher (i) is robust against various audio degradation and (ii) enable us to train a high-quality text-to-speech (TTS) model from restored speech samples collected from the Web. Audio samples are available at our demo page: google.github.io/df-conformer/miipher/
[ { "version": "v1", "created": "Fri, 3 Mar 2023 01:57:16 GMT" }, { "version": "v2", "created": "Mon, 14 Aug 2023 09:22:18 GMT" } ]
2023-08-15T00:00:00
[ [ "Koizumi", "Yuma", "" ], [ "Zen", "Heiga", "" ], [ "Karita", "Shigeki", "" ], [ "Ding", "Yifan", "" ], [ "Yatabe", "Kohei", "" ], [ "Morioka", "Nobuyuki", "" ], [ "Zhang", "Yu", "" ], [ "Han", "Wei", "" ], [ "Bapna", "Ankur", "" ], [ "Bacchiani", "Michiel", "" ] ]
new_dataset
0.997911
2303.06007
Bilal Farooq
Nael Alsaleh and Bilal Farooq
Sustainability Analysis Framework for On-Demand Public Transit Systems
null
null
null
null
cs.CY
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
There is an increased interest from transit agencies to replace fixed-route transit services with on-demand public transits (ODT). However, it is still unclear when and where such a service is efficient and sustainable. To this end, we provide a comprehensive framework for assessing the sustainability of ODT systems from the perspective of overall efficiency, environmental footprint, and social equity and inclusion. The proposed framework is illustrated by applying it to the Town of Innisfil, Ontario, where an ODT system has been implemented since 2017. It can be concluded that when there is adequate supply and no surge pricing, crowdsourced ODTs are the most cost-effective transit system when the demand is below 3.37 riders/km2/day. With surge pricing applied to crowdsourced ODTs, hybrid systems become the most cost-effective transit solution when demand ranges between 1.18 and 3.37 riders/km2/day. The use of private vehicles is more environmentally sustainable than providing public transit service at all demand levels below 3.37 riders/km2/day. However, the electrification of the public transit fleet along with optimized charging strategies can reduce total yearly GHG emissions by more than 98%. Furthermore, transit systems have similar equity distributions for waiting and in-vehicle travel times.
[ { "version": "v1", "created": "Fri, 10 Mar 2023 16:09:51 GMT" }, { "version": "v2", "created": "Fri, 11 Aug 2023 23:42:03 GMT" } ]
2023-08-15T00:00:00
[ [ "Alsaleh", "Nael", "" ], [ "Farooq", "Bilal", "" ] ]
new_dataset
0.994891
2303.07274
Yonatan Bitton
Nitzan Bitton-Guetta, Yonatan Bitton, Jack Hessel, Ludwig Schmidt, Yuval Elovici, Gabriel Stanovsky, Roy Schwartz
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images
Accepted to ICCV 2023. Website: whoops-benchmark.github.io
null
null
null
cs.CV cs.AI cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Weird, unusual, and uncanny images pique the curiosity of observers because they challenge commonsense. For example, an image released during the 2022 world cup depicts the famous soccer stars Lionel Messi and Cristiano Ronaldo playing chess, which playfully violates our expectation that their competition should occur on the football field. Humans can easily recognize and interpret these unconventional images, but can AI models do the same? We introduce WHOOPS!, a new dataset and benchmark for visual commonsense. The dataset is comprised of purposefully commonsense-defying images created by designers using publicly-available image generation tools like Midjourney. We consider several tasks posed over the dataset. In addition to image captioning, cross-modal matching, and visual question answering, we introduce a difficult explanation generation task, where models must identify and explain why a given image is unusual. Our results show that state-of-the-art models such as GPT3 and BLIP2 still lag behind human performance on WHOOPS!. We hope our dataset will inspire the development of AI models with stronger visual commonsense reasoning abilities. Data, models and code are available at the project website: whoops-benchmark.github.io
[ { "version": "v1", "created": "Mon, 13 Mar 2023 16:49:43 GMT" }, { "version": "v2", "created": "Tue, 14 Mar 2023 21:30:06 GMT" }, { "version": "v3", "created": "Thu, 13 Jul 2023 16:36:38 GMT" }, { "version": "v4", "created": "Sat, 12 Aug 2023 22:37:31 GMT" } ]
2023-08-15T00:00:00
[ [ "Bitton-Guetta", "Nitzan", "" ], [ "Bitton", "Yonatan", "" ], [ "Hessel", "Jack", "" ], [ "Schmidt", "Ludwig", "" ], [ "Elovici", "Yuval", "" ], [ "Stanovsky", "Gabriel", "" ], [ "Schwartz", "Roy", "" ] ]
new_dataset
0.997941
2303.08597
Thanh Nhat Huy Nguyen
Huy Nguyen, Kien Nguyen, Sridha Sridharan, Clinton Fookes
Aerial-Ground Person Re-ID
Published on IEEE International Conference on Multimedia and Expo 2023 (ICME2023)
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Person re-ID matches persons across multiple non-overlapping cameras. Despite the increasing deployment of airborne platforms in surveillance, current existing person re-ID benchmarks' focus is on ground-ground matching and very limited efforts on aerial-aerial matching. We propose a new benchmark dataset - AG-ReID, which performs person re-ID matching in a new setting: across aerial and ground cameras. Our dataset contains 21,983 images of 388 identities and 15 soft attributes for each identity. The data was collected by a UAV flying at altitudes between 15 to 45 meters and a ground-based CCTV camera on a university campus. Our dataset presents a novel elevated-viewpoint challenge for person re-ID due to the significant difference in person appearance across these cameras. We propose an explainable algorithm to guide the person re-ID model's training with soft attributes to address this challenge. Experiments demonstrate the efficacy of our method on the aerial-ground person re-ID task. The dataset will be published and the baseline codes will be open-sourced at https://github.com/huynguyen792/AG-ReID to facilitate research in this area.
[ { "version": "v1", "created": "Wed, 15 Mar 2023 13:07:21 GMT" }, { "version": "v2", "created": "Thu, 16 Mar 2023 09:32:42 GMT" }, { "version": "v3", "created": "Thu, 23 Mar 2023 00:36:08 GMT" }, { "version": "v4", "created": "Mon, 27 Mar 2023 07:56:21 GMT" }, { "version": "v5", "created": "Mon, 14 Aug 2023 04:44:50 GMT" } ]
2023-08-15T00:00:00
[ [ "Nguyen", "Huy", "" ], [ "Nguyen", "Kien", "" ], [ "Sridharan", "Sridha", "" ], [ "Fookes", "Clinton", "" ] ]
new_dataset
0.999823
2303.09695
Sauradip Nag
Sauradip Nag, Anran Qi, Xiatian Zhu and Ariel Shamir
PersonalTailor: Personalizing 2D Pattern Design from 3D Garment Point Clouds
Technical Report
null
null
null
cs.CV cs.GR cs.MM
http://creativecommons.org/licenses/by-nc-nd/4.0/
Garment pattern design aims to convert a 3D garment to the corresponding 2D panels and their sewing structure. Existing methods rely either on template fitting with heuristics and prior assumptions, or on model learning with complicated shape parameterization. Importantly, both approaches do not allow for personalization of the output garment, which today has increasing demands. To fill this demand, we introduce PersonalTailor: a personalized 2D pattern design method, where the user can input specific constraints or demands (in language or sketch) for personal 2D panel fabrication from 3D point clouds. PersonalTailor first learns a multi-modal panel embeddings based on unsupervised cross-modal association and attentive fusion. It then predicts a binary panel masks individually using a transformer encoder-decoder framework. Extensive experiments show that our PersonalTailor excels on both personalized and standard pattern fabrication tasks.
[ { "version": "v1", "created": "Fri, 17 Mar 2023 00:03:38 GMT" }, { "version": "v2", "created": "Fri, 11 Aug 2023 20:07:48 GMT" } ]
2023-08-15T00:00:00
[ [ "Nag", "Sauradip", "" ], [ "Qi", "Anran", "" ], [ "Zhu", "Xiatian", "" ], [ "Shamir", "Ariel", "" ] ]
new_dataset
0.997784
2303.16986
Kamran Shafafi
Kamran Shafafi, Eduardo Nuno Almeida, Andr\'e Coelho, Helder Fontes, Manuel Ricardo, Rui Campos
UAV-Assisted Wireless Communications: An Experimental Analysis of A2G and G2A Channels
null
null
null
null
cs.NI eess.SP
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Unmanned Aerial Vehicles (UAVs) offer promising potential as communications node carriers, providing on-demand wireless connectivity to users. While existing literature presents various wireless channel models, it often overlooks the impact of UAV heading. This paper provides an experimental characterization of the Air-to-Ground (A2G) and Ground-to-Air (G2A) wireless channels in an open environment with no obstacles nor interference, considering the distance and the UAV heading. We analyze the received signal strength indicator and the TCP throughput between a ground user and a UAV, covering distances between 50~m and 500~m, and considering different UAV headings. Additionally, we characterize the antenna's radiation pattern based on UAV headings. The paper provides valuable perspectives on the capabilities of UAVs in offering on-demand and dynamic wireless connectivity, as well as highlights the significance of considering UAV heading and antenna configurations in real-world scenarios.
[ { "version": "v1", "created": "Wed, 29 Mar 2023 19:26:38 GMT" }, { "version": "v2", "created": "Tue, 6 Jun 2023 08:40:24 GMT" }, { "version": "v3", "created": "Sat, 12 Aug 2023 10:07:53 GMT" } ]
2023-08-15T00:00:00
[ [ "Shafafi", "Kamran", "" ], [ "Almeida", "Eduardo Nuno", "" ], [ "Coelho", "André", "" ], [ "Fontes", "Helder", "" ], [ "Ricardo", "Manuel", "" ], [ "Campos", "Rui", "" ] ]
new_dataset
0.997793
2304.08842
Sicen Guo
Sicen Guo, Jiahang Li, Shuai Su, Yi Feng, Dacheng Zhou, Chen Chen, Denghuang Zhang, Xingyi Zhu, Qijun Chen, Rui Fan
UDTIRI: An Open-Source Intelligent Road Inspection Benchmark Suite
Database webpage: https://www.udtiri.com/, Kaggle webpage: https://www.kaggle.com/datasets/jiahangli617/udtiri
null
null
null
cs.CV cs.AI cs.LG cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
It is seen that there is enormous potential to leverage powerful deep learning methods in the emerging field of urban digital twins. It is particularly in the area of intelligent road inspection where there is currently limited research and data available. To facilitate progress in this field, we have developed a well-labeled road pothole dataset named Urban Digital Twins Intelligent Road Inspection (UDTIRI) dataset. We hope this dataset will enable the use of powerful deep learning methods in urban road inspection, providing algorithms with a more comprehensive understanding of the scene and maximizing their potential. Our dataset comprises 1000 images of potholes, captured in various scenarios with different lighting and humidity conditions. Our intention is to employ this dataset for object detection, semantic segmentation, and instance segmentation tasks. Our team has devoted significant effort to conducting a detailed statistical analysis, and benchmarking a selection of representative algorithms from recent years. We also provide a multi-task platform for researchers to fully exploit the performance of various algorithms with the support of UDTIRI dataset.
[ { "version": "v1", "created": "Tue, 18 Apr 2023 09:13:52 GMT" }, { "version": "v2", "created": "Sun, 13 Aug 2023 11:31:34 GMT" } ]
2023-08-15T00:00:00
[ [ "Guo", "Sicen", "" ], [ "Li", "Jiahang", "" ], [ "Su", "Shuai", "" ], [ "Feng", "Yi", "" ], [ "Zhou", "Dacheng", "" ], [ "Chen", "Chen", "" ], [ "Zhang", "Denghuang", "" ], [ "Zhu", "Xingyi", "" ], [ "Chen", "Qijun", "" ], [ "Fan", "Rui", "" ] ]
new_dataset
0.999793
2304.12687
Ligong Wang
Amos Lapidoth and Ligong Wang
State-Dependent DMC with a Causal Helper
To appear in the IEEE Transactions on Information Theory
null
null
null
cs.IT math.IT
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
A memoryless state sequence governing the behavior of a memoryless state-dependent channel is to be described causally to an encoder wishing to communicate over said channel. Given the maximal-allowed description rate, we seek the description that maximizes the Shannon capacity. It is shown that the maximum need not be achieved by a memoryless (symbol-by-symbol) description. Such descriptions are, however, optimal when the receiver is cognizant of the state sequence or when the description is allowed to depend on the message. For other cases, a block-Markov scheme with backward decoding is proposed.
[ { "version": "v1", "created": "Tue, 25 Apr 2023 09:42:11 GMT" }, { "version": "v2", "created": "Sun, 13 Aug 2023 06:46:04 GMT" } ]
2023-08-15T00:00:00
[ [ "Lapidoth", "Amos", "" ], [ "Wang", "Ligong", "" ] ]
new_dataset
0.98749
2305.01643
Shengyu Huang
Shengyu Huang, Zan Gojcic, Zian Wang, Francis Williams, Yoni Kasten, Sanja Fidler, Konrad Schindler, Or Litany
Neural LiDAR Fields for Novel View Synthesis
ICCV 2023 - camera ready. Project page: https://research.nvidia.com/labs/toronto-ai/nfl/
null
null
null
cs.CV
http://creativecommons.org/licenses/by-nc-sa/4.0/
We present Neural Fields for LiDAR (NFL), a method to optimise a neural field scene representation from LiDAR measurements, with the goal of synthesizing realistic LiDAR scans from novel viewpoints. NFL combines the rendering power of neural fields with a detailed, physically motivated model of the LiDAR sensing process, thus enabling it to accurately reproduce key sensor behaviors like beam divergence, secondary returns, and ray dropping. We evaluate NFL on synthetic and real LiDAR scans and show that it outperforms explicit reconstruct-then-simulate methods as well as other NeRF-style methods on LiDAR novel view synthesis task. Moreover, we show that the improved realism of the synthesized views narrows the domain gap to real scans and translates to better registration and semantic segmentation performance.
[ { "version": "v1", "created": "Tue, 2 May 2023 17:55:38 GMT" }, { "version": "v2", "created": "Sun, 13 Aug 2023 09:25:18 GMT" } ]
2023-08-15T00:00:00
[ [ "Huang", "Shengyu", "" ], [ "Gojcic", "Zan", "" ], [ "Wang", "Zian", "" ], [ "Williams", "Francis", "" ], [ "Kasten", "Yoni", "" ], [ "Fidler", "Sanja", "" ], [ "Schindler", "Konrad", "" ], [ "Litany", "Or", "" ] ]
new_dataset
0.977309
2305.09419
Gilbert Netzer
Gilbert Netzer and Stefano Markidis
QHDL: a Low-Level Circuit Description Language for Quantum Computing
4 pages, 7 figures, to be published in Proceedings of the 20th ACM International Conference on Computing Frontiers, May 9-11, 2023, Bologna, Italy
null
10.1145/3587135.3592191
null
cs.ET
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper proposes a descriptive language called QHDL, akin to VHDL, to program gate-based quantum computing systems. Unlike other popular quantum programming languages, QHDL targets low-level quantum computing programming and aims to provide a common framework for programming FPGAs and gate-based quantum computing systems. The paper presents an initial implementation and design principles of the QHDL framework, including a compiler and quantum computer simulator. We discuss the challenges of low-level integration of streaming models and quantum computing for programming FPGAs and gate-based quantum computing systems.
[ { "version": "v1", "created": "Tue, 16 May 2023 13:18:27 GMT" } ]
2023-08-15T00:00:00
[ [ "Netzer", "Gilbert", "" ], [ "Markidis", "Stefano", "" ] ]
new_dataset
0.99961
2306.02898
Shuyu Yang
Shuyu Yang, Yinan Zhou, Yaxiong Wang, Yujiao Wu, Li Zhu, Zhedong Zheng
Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark
null
null
null
null
cs.CV cs.MM
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
In this paper, we introduce a large Multi-Attribute and Language Search dataset for text-based person retrieval, called MALS, and explore the feasibility of performing pre-training on both attribute recognition and image-text matching tasks in one stone. In particular, MALS contains 1,510,330 image-text pairs, which is about 37.5 times larger than prevailing CUHK-PEDES, and all images are annotated with 27 attributes. Considering the privacy concerns and annotation costs, we leverage the off-the-shelf diffusion models to generate the dataset. To verify the feasibility of learning from the generated data, we develop a new joint Attribute Prompt Learning and Text Matching Learning (APTM) framework, considering the shared knowledge between attribute and text. As the name implies, APTM contains an attribute prompt learning stream and a text matching learning stream. (1) The attribute prompt learning leverages the attribute prompts for image-attribute alignment, which enhances the text matching learning. (2) The text matching learning facilitates the representation learning on fine-grained details, and in turn, boosts the attribute prompt learning. Extensive experiments validate the effectiveness of the pre-training on MALS, achieving state-of-the-art retrieval performance via APTM on three challenging real-world benchmarks. In particular, APTM achieves a consistent improvement of +6.96%, +7.68%, and +16.95% Recall@1 accuracy on CUHK-PEDES, ICFG-PEDES, and RSTPReid datasets by a clear margin, respectively.
[ { "version": "v1", "created": "Mon, 5 Jun 2023 14:06:24 GMT" }, { "version": "v2", "created": "Tue, 6 Jun 2023 06:42:56 GMT" }, { "version": "v3", "created": "Fri, 11 Aug 2023 11:13:08 GMT" }, { "version": "v4", "created": "Mon, 14 Aug 2023 07:37:27 GMT" } ]
2023-08-15T00:00:00
[ [ "Yang", "Shuyu", "" ], [ "Zhou", "Yinan", "" ], [ "Wang", "Yaxiong", "" ], [ "Wu", "Yujiao", "" ], [ "Zhu", "Li", "" ], [ "Zheng", "Zhedong", "" ] ]
new_dataset
0.987319
2306.07705
Zhongxiang Sun
Zhongxiang Sun and Zihua Si and Xiaoxue Zang and Dewei Leng and Yanan Niu and Yang Song and Xiao Zhang and Jun Xu
KuaiSAR: A Unified Search And Recommendation Dataset
CIKM 2023 resource track
null
null
null
cs.IR
http://creativecommons.org/licenses/by-nc-sa/4.0/
The confluence of Search and Recommendation (S&R) services is vital to online services, including e-commerce and video platforms. The integration of S&R modeling is a highly intuitive approach adopted by industry practitioners. However, there is a noticeable lack of research conducted in this area within academia, primarily due to the absence of publicly available datasets. Consequently, a substantial gap has emerged between academia and industry regarding research endeavors in joint optimization using user behavior data from both S&R services. To bridge this gap, we introduce the first large-scale, real-world dataset KuaiSAR of integrated Search And Recommendation behaviors collected from Kuaishou, a leading short-video app in China with over 350 million daily active users. Previous research in this field has predominantly employed publicly available semi-synthetic datasets and simulated, with artificially fabricated search behaviors. Distinct from previous datasets, KuaiSAR contains genuine user behaviors, including the occurrence of each interaction within either search or recommendation service, and the users' transitions between the two services. This work aids in joint modeling of S&R, and utilizing search data for recommender systems (and recommendation data for search engines). Furthermore, due to the various feedback labels associated with user-video interactions, KuaiSAR also supports a broad range of tasks, including intent recommendation, multi-task learning, and modeling of long sequential multi-behavioral patterns. We believe this dataset will serve as a catalyst for innovative research and bridge the gap between academia and industry in understanding the S&R services in practical, real-world applications.
[ { "version": "v1", "created": "Tue, 13 Jun 2023 11:46:37 GMT" }, { "version": "v2", "created": "Wed, 14 Jun 2023 11:18:36 GMT" }, { "version": "v3", "created": "Sun, 18 Jun 2023 07:49:58 GMT" }, { "version": "v4", "created": "Mon, 14 Aug 2023 03:48:45 GMT" } ]
2023-08-15T00:00:00
[ [ "Sun", "Zhongxiang", "" ], [ "Si", "Zihua", "" ], [ "Zang", "Xiaoxue", "" ], [ "Leng", "Dewei", "" ], [ "Niu", "Yanan", "" ], [ "Song", "Yang", "" ], [ "Zhang", "Xiao", "" ], [ "Xu", "Jun", "" ] ]
new_dataset
0.996159
2306.09011
Kevis-Kokitsi Maninis
Kevis-Kokitsi Maninis, Stefan Popov, Matthias Nie{\ss}ner, Vittorio Ferrari
CAD-Estate: Large-scale CAD Model Annotation in RGB Videos
Project page: https://github.com/google-research/cad-estate
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We propose a method for annotating videos of complex multi-object scenes with a globally-consistent 3D representation of the objects. We annotate each object with a CAD model from a database, and place it in the 3D coordinate frame of the scene with a 9-DoF pose transformation. Our method is semi-automatic and works on commonly-available RGB videos, without requiring a depth sensor. Many steps are performed automatically, and the tasks performed by humans are simple, well-specified, and require only limited reasoning in 3D. This makes them feasible for crowd-sourcing and has allowed us to construct a large-scale dataset by annotating real-estate videos from YouTube. Our dataset CAD-Estate offers 101k instances of 12k unique CAD models placed in the 3D representations of 20k videos. In comparison to Scan2CAD, the largest existing dataset with CAD model annotations on real scenes, CAD-Estate has 7x more instances and 4x more unique CAD models. We showcase the benefits of pre-training a Mask2CAD model on CAD-Estate for the task of automatic 3D object reconstruction and pose estimation, demonstrating that it leads to performance improvements on the popular Scan2CAD benchmark. The dataset is available at https://github.com/google-research/cad-estate.
[ { "version": "v1", "created": "Thu, 15 Jun 2023 10:12:02 GMT" }, { "version": "v2", "created": "Mon, 14 Aug 2023 12:16:53 GMT" } ]
2023-08-15T00:00:00
[ [ "Maninis", "Kevis-Kokitsi", "" ], [ "Popov", "Stefan", "" ], [ "Nießner", "Matthias", "" ], [ "Ferrari", "Vittorio", "" ] ]
new_dataset
0.973932
2307.01482
Tong Nie
Tong Nie, Guoyang Qin, Lijun Sun, Yunpeng Wang, Jian Sun
Nexus sine qua non: Essentially Connected Networks for Traffic Forecasting
null
null
null
null
cs.LG
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Spatiotemporal graph neural networks (STGNNs) have emerged as a leading approach for learning representations and forecasting on traffic datasets with underlying topological and correlational structures. However, current STGNNs use intricate techniques with high complexities to capture these structures, making them difficult to understand and scale. The existence of simple yet efficient architectures remains an open question. Upon closer examination, we find what lies at the core of STGNN's representations are certain forms of spatiotemporal contextualization. In light of this, we design Nexus sine qua non (NexuSQN), an essentially connected network built on an efficient message-passing backbone. NexuSQN simply uses learnable "where" and "when" locators for the aforementioned contextualization and omits any intricate components such as RNNs, Transformers, and diffusion convolutions. Results show that NexuSQN outperforms intricately designed benchmarks in terms of size, computational efficiency, and accuracy. This suggests a promising future for developing simple yet efficient neural predictors.
[ { "version": "v1", "created": "Tue, 4 Jul 2023 05:19:19 GMT" }, { "version": "v2", "created": "Mon, 24 Jul 2023 02:40:29 GMT" }, { "version": "v3", "created": "Wed, 2 Aug 2023 07:39:53 GMT" }, { "version": "v4", "created": "Sun, 13 Aug 2023 08:42:08 GMT" } ]
2023-08-15T00:00:00
[ [ "Nie", "Tong", "" ], [ "Qin", "Guoyang", "" ], [ "Sun", "Lijun", "" ], [ "Wang", "Yunpeng", "" ], [ "Sun", "Jian", "" ] ]
new_dataset
0.997877
2307.06281
Haodong Duan
Yuan Liu, Haodong Duan, Yuanhan Zhang, Bo Li, Songyang Zhang, Wangbo Zhao, Yike Yuan, Jiaqi Wang, Conghui He, Ziwei Liu, Kai Chen, Dahua Lin
MMBench: Is Your Multi-modal Model an All-around Player?
null
null
null
null
cs.CV cs.CL
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Large vision-language models have recently achieved remarkable progress, exhibiting great perception and reasoning abilities concerning visual information. However, how to effectively evaluate these large vision-language models remains a major obstacle, hindering future model development. Traditional benchmarks like VQAv2 or COCO Caption provide quantitative performance measurements but suffer from a lack of fine-grained ability assessment and non-robust evaluation metrics. Recent subjective benchmarks, such as OwlEval, offer comprehensive evaluations of a model's abilities by incorporating human labor, but they are not scalable and display significant bias. In response to these challenges, we propose MMBench, a novel multi-modality benchmark. MMBench methodically develops a comprehensive evaluation pipeline, primarily comprised of two elements. The first element is a meticulously curated dataset that surpasses existing similar benchmarks in terms of the number and variety of evaluation questions and abilities. The second element introduces a novel CircularEval strategy and incorporates the use of ChatGPT. This implementation is designed to convert free-form predictions into pre-defined choices, thereby facilitating a more robust evaluation of the model's predictions. MMBench is a systematically-designed objective benchmark for robustly evaluating the various abilities of vision-language models. We hope MMBench will assist the research community in better evaluating their models and encourage future advancements in this domain. Project page: https://opencompass.org.cn/mmbench.
[ { "version": "v1", "created": "Wed, 12 Jul 2023 16:23:09 GMT" }, { "version": "v2", "created": "Wed, 26 Jul 2023 16:02:57 GMT" }, { "version": "v3", "created": "Sun, 13 Aug 2023 13:12:47 GMT" } ]
2023-08-15T00:00:00
[ [ "Liu", "Yuan", "" ], [ "Duan", "Haodong", "" ], [ "Zhang", "Yuanhan", "" ], [ "Li", "Bo", "" ], [ "Zhang", "Songyang", "" ], [ "Zhao", "Wangbo", "" ], [ "Yuan", "Yike", "" ], [ "Wang", "Jiaqi", "" ], [ "He", "Conghui", "" ], [ "Liu", "Ziwei", "" ], [ "Chen", "Kai", "" ], [ "Lin", "Dahua", "" ] ]
new_dataset
0.99914
2307.06505
Shanliang Yao
Shanliang Yao, Runwei Guan, Zhaodong Wu, Yi Ni, Zile Huang, Zixian Zhang, Yong Yue, Weiping Ding, Eng Gee Lim, Hyungjoon Seo, Ka Lok Man, Xiaohui Zhu, Yutao Yue
WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmark for Autonomous Driving on Water Surfaces
null
null
null
null
cs.CV cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Autonomous driving on water surfaces plays an essential role in executing hazardous and time-consuming missions, such as maritime surveillance, survivors rescue, environmental monitoring, hydrography mapping and waste cleaning. This work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset for autonomous driving on water surfaces. Equipped with a 4D radar and a monocular camera, our Unmanned Surface Vehicle (USV) proffers all-weather solutions for discerning object-related information, including color, shape, texture, range, velocity, azimuth, and elevation. Focusing on typical static and dynamic objects on water surfaces, we label the camera images and radar point clouds at pixel-level and point-level, respectively. In addition to basic perception tasks, such as object detection, instance segmentation and semantic segmentation, we also provide annotations for free-space segmentation and waterline segmentation. Leveraging the multi-task and multi-modal data, we conduct benchmark experiments on the uni-modality of radar and camera, as well as the fused modalities. Experimental results demonstrate that 4D radar-camera fusion can considerably improve the accuracy and robustness of perception on water surfaces, especially in adverse lighting and weather conditions. WaterScenes dataset is public on https://waterscenes.github.io.
[ { "version": "v1", "created": "Thu, 13 Jul 2023 01:05:12 GMT" }, { "version": "v2", "created": "Mon, 14 Aug 2023 08:52:02 GMT" } ]
2023-08-15T00:00:00
[ [ "Yao", "Shanliang", "" ], [ "Guan", "Runwei", "" ], [ "Wu", "Zhaodong", "" ], [ "Ni", "Yi", "" ], [ "Huang", "Zile", "" ], [ "Zhang", "Zixian", "" ], [ "Yue", "Yong", "" ], [ "Ding", "Weiping", "" ], [ "Lim", "Eng Gee", "" ], [ "Seo", "Hyungjoon", "" ], [ "Man", "Ka Lok", "" ], [ "Zhu", "Xiaohui", "" ], [ "Yue", "Yutao", "" ] ]
new_dataset
0.999815
2307.08602
Hiroyasu Tsukamoto
Hiroyasu Tsukamoto and Benjamin Rivi\`ere and Changrak Choi and Amir Rahmani and Soon-Jo Chung
CaRT: Certified Safety and Robust Tracking in Learning-based Motion Planning for Multi-Agent Systems
IEEE Conference on Decision and Control (CDC), Preprint Version, Accepted July, 2023
null
null
null
cs.RO cs.LG cs.MA cs.SY eess.SY math.OC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The key innovation of our analytical method, CaRT, lies in establishing a new hierarchical, distributed architecture to guarantee the safety and robustness of a given learning-based motion planning policy. First, in a nominal setting, the analytical form of our CaRT safety filter formally ensures safe maneuvers of nonlinear multi-agent systems, optimally with minimal deviation from the learning-based policy. Second, in off-nominal settings, the analytical form of our CaRT robust filter optimally tracks the certified safe trajectory, generated by the previous layer in the hierarchy, the CaRT safety filter. We show using contraction theory that CaRT guarantees safety and the exponential boundedness of the trajectory tracking error, even under the presence of deterministic and stochastic disturbance. Also, the hierarchical nature of CaRT enables enhancing its robustness for safety just by its superior tracking to the certified safe trajectory, thereby making it suitable for off-nominal scenarios with large disturbances. This is a major distinction from conventional safety function-driven approaches, where the robustness originates from the stability of a safe set, which could pull the system over-conservatively to the interior of the safe set. Our log-barrier formulation in CaRT allows for its distributed implementation in multi-agent settings. We demonstrate the effectiveness of CaRT in several examples of nonlinear motion planning and control problems, including optimal, multi-spacecraft reconfiguration.
[ { "version": "v1", "created": "Thu, 13 Jul 2023 21:51:29 GMT" }, { "version": "v2", "created": "Sun, 13 Aug 2023 20:36:46 GMT" } ]
2023-08-15T00:00:00
[ [ "Tsukamoto", "Hiroyasu", "" ], [ "Rivière", "Benjamin", "" ], [ "Choi", "Changrak", "" ], [ "Rahmani", "Amir", "" ], [ "Chung", "Soon-Jo", "" ] ]
new_dataset
0.993446
2307.09531
Kai Huang
Kai Huang, Junqiao Zhao, Zhongyang Zhu, Chen Ye, Tiantian Feng
LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation
8 pages, 4 figures
null
null
null
cs.RO
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Local geometric information, i.e. normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for data association, which further determines the direction of optimization and ultimately affects the accuracy of localization. However, estimating normal and distribution of points are time-consuming tasks even with the assistance of kdtree or volumetric maps. To achieve fast normal estimation, we look into the structure of LiDAR scan and propose a ring-based fast approximate least squares (Ring FALS) method. With the Ring structural information, estimating the normal requires only the range information of the points when a new scan arrives. To efficiently estimate the distribution of points, we extend the ikd-tree to manage the map in voxels and update the distribution of points in each voxel incrementally while maintaining its consistency with the normal estimation. We further fix the distribution after its convergence to balance the time consumption and the correctness of representation. Based on the extracted and maintained local geometric information, we devise a robust and accurate hierarchical data association scheme where point-to-surfel association is prioritized over point-to-plane. Extensive experiments on diverse public datasets demonstrate the advantages of our system compared to other state-of-the-art methods. Our open source implementation is available at https://github.com/tiev-tongji/LOG-LIO.
[ { "version": "v1", "created": "Tue, 18 Jul 2023 18:20:56 GMT" }, { "version": "v2", "created": "Mon, 14 Aug 2023 01:47:50 GMT" } ]
2023-08-15T00:00:00
[ [ "Huang", "Kai", "" ], [ "Zhao", "Junqiao", "" ], [ "Zhu", "Zhongyang", "" ], [ "Ye", "Chen", "" ], [ "Feng", "Tiantian", "" ] ]
new_dataset
0.998785
2307.10818
Dongwei Xiao
Dongwei Xiao, Zhibo Liu, and Shuai Wang
PHYFU: Fuzzing Modern Physics Simulation Engines
This paper is accepted at The 38th IEEE/ACM International Conference on Automated Software Engineering, a.k.a. ASE 2023. Please cite the published version as soon as this paper appears in the conference publications
null
null
null
cs.SE
http://creativecommons.org/licenses/by/4.0/
A physical simulation engine (PSE) is a software system that simulates physical environments and objects. Modern PSEs feature both forward and backward simulations, where the forward phase predicts the behavior of a simulated system, and the backward phase provides gradients (guidance) for learning-based control tasks, such as a robot arm learning to fetch items. This way, modern PSEs show promising support for learning-based control methods. To date, PSEs have been largely used in various high-profitable, commercial applications, such as games, movies, virtual reality (VR), and robotics. Despite the prosperous development and usage of PSEs by academia and industrial manufacturers such as Google and NVIDIA, PSEs may produce incorrect simulations, which may lead to negative results, from poor user experience in entertainment to accidents in robotics-involved manufacturing and surgical operations. This paper introduces PHYFU, a fuzzing framework designed specifically for PSEs to uncover errors in both forward and backward simulation phases. PHYFU mutates initial states and asserts if the PSE under test behaves consistently with respect to basic Physics Laws (PLs). We further use feedback-driven test input scheduling to guide and accelerate the search for errors. Our study of four PSEs covers mainstream industrial vendors (Google and NVIDIA) as well as academic products. We successfully uncover over 5K error-triggering inputs that generate incorrect simulation results spanning across the whole software stack of PSEs.
[ { "version": "v1", "created": "Thu, 20 Jul 2023 12:26:50 GMT" }, { "version": "v2", "created": "Mon, 14 Aug 2023 03:58:59 GMT" } ]
2023-08-15T00:00:00
[ [ "Xiao", "Dongwei", "" ], [ "Liu", "Zhibo", "" ], [ "Wang", "Shuai", "" ] ]
new_dataset
0.999749
2308.01686
Zhiwei Zhang
Zhiwei Zhang, Zhizhong Zhang, Qian Yu, Ran Yi, Yuan Xie and Lizhuang Ma
LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware Alignment
Accepted as ICCV 2023 paper
null
null
null
cs.CV cs.AI
http://creativecommons.org/licenses/by/4.0/
3D panoptic segmentation is a challenging perception task that requires both semantic segmentation and instance segmentation. In this task, we notice that images could provide rich texture, color, and discriminative information, which can complement LiDAR data for evident performance improvement, but their fusion remains a challenging problem. To this end, we propose LCPS, the first LiDAR-Camera Panoptic Segmentation network. In our approach, we conduct LiDAR-Camera fusion in three stages: 1) an Asynchronous Compensation Pixel Alignment (ACPA) module that calibrates the coordinate misalignment caused by asynchronous problems between sensors; 2) a Semantic-Aware Region Alignment (SARA) module that extends the one-to-one point-pixel mapping to one-to-many semantic relations; 3) a Point-to-Voxel feature Propagation (PVP) module that integrates both geometric and semantic fusion information for the entire point cloud. Our fusion strategy improves about 6.9% PQ performance over the LiDAR-only baseline on NuScenes dataset. Extensive quantitative and qualitative experiments further demonstrate the effectiveness of our novel framework. The code will be released at https://github.com/zhangzw12319/lcps.git.
[ { "version": "v1", "created": "Thu, 3 Aug 2023 10:57:58 GMT" }, { "version": "v2", "created": "Fri, 11 Aug 2023 18:32:54 GMT" } ]
2023-08-15T00:00:00
[ [ "Zhang", "Zhiwei", "" ], [ "Zhang", "Zhizhong", "" ], [ "Yu", "Qian", "" ], [ "Yi", "Ran", "" ], [ "Xie", "Yuan", "" ], [ "Ma", "Lizhuang", "" ] ]
new_dataset
0.991069
2308.01861
Xueying Du
Xueying Du, Mingwei Liu, Kaixin Wang, Hanlin Wang, Junwei Liu, Yixuan Chen, Jiayi Feng, Chaofeng Sha, Xin Peng, Yiling Lou
ClassEval: A Manually-Crafted Benchmark for Evaluating LLMs on Class-level Code Generation
null
null
null
null
cs.CL cs.AI
http://creativecommons.org/licenses/by-nc-nd/4.0/
In this work, we make the first attempt to evaluate LLMs in a more challenging code generation scenario, i.e. class-level code generation. We first manually construct the first class-level code generation benchmark ClassEval of 100 class-level Python code generation tasks with approximately 500 person-hours. Based on it, we then perform the first study of 11 state-of-the-art LLMs on class-level code generation. Based on our results, we have the following main findings. First, we find that all existing LLMs show much worse performance on class-level code generation compared to on standalone method-level code generation benchmarks like HumanEval; and the method-level coding ability cannot equivalently reflect the class-level coding ability among LLMs. Second, we find that GPT-4 and GPT-3.5 still exhibit dominate superior than other LLMs on class-level code generation, and the second-tier models includes Instruct-Starcoder, Instruct-Codegen, and Wizardcoder with very similar performance. Third, we find that generating the entire class all at once (i.e. holistic generation strategy) is the best generation strategy only for GPT-4 and GPT-3.5, while method-by-method generation (i.e. incremental and compositional) is better strategies for the other models with limited ability of understanding long instructions and utilizing the middle information. Lastly, we find the limited model ability of generating method-dependent code and discuss the frequent error types in generated classes. Our benchmark is available at https://github.com/FudanSELab/ClassEval.
[ { "version": "v1", "created": "Thu, 3 Aug 2023 16:31:02 GMT" }, { "version": "v2", "created": "Mon, 14 Aug 2023 09:07:00 GMT" } ]
2023-08-15T00:00:00
[ [ "Du", "Xueying", "" ], [ "Liu", "Mingwei", "" ], [ "Wang", "Kaixin", "" ], [ "Wang", "Hanlin", "" ], [ "Liu", "Junwei", "" ], [ "Chen", "Yixuan", "" ], [ "Feng", "Jiayi", "" ], [ "Sha", "Chaofeng", "" ], [ "Peng", "Xin", "" ], [ "Lou", "Yiling", "" ] ]
new_dataset
0.975289
2308.04498
Hao Fei
Yiyun Xiong, Mengwei Dai, Fei Li, Hao Fei, Bobo Li, Shengqiong Wu, Donghong Ji, Chong Teng
DialogRE^C+: An Extension of DialogRE to Investigate How Much Coreference Helps Relation Extraction in Dialogs
Accepted by NLPCC 2023
null
null
null
cs.CL
http://creativecommons.org/licenses/by-nc-nd/4.0/
Dialogue relation extraction (DRE) that identifies the relations between argument pairs in dialogue text, suffers much from the frequent occurrence of personal pronouns, or entity and speaker coreference. This work introduces a new benchmark dataset DialogRE^C+, introducing coreference resolution into the DRE scenario. With the aid of high-quality coreference knowledge, the reasoning of argument relations is expected to be enhanced. In DialogRE^C+ dataset, we manually annotate total 5,068 coreference chains over 36,369 argument mentions based on the existing DialogRE data, where four different coreference chain types namely speaker chain, person chain, location chain and organization chain are explicitly marked. We further develop 4 coreference-enhanced graph-based DRE models, which learn effective coreference representations for improving the DRE task. We also train a coreference resolution model based on our annotations and evaluate the effect of automatically extracted coreference chains demonstrating the practicality of our dataset and its potential to other domains and tasks.
[ { "version": "v1", "created": "Tue, 8 Aug 2023 18:03:29 GMT" }, { "version": "v2", "created": "Sat, 12 Aug 2023 06:12:36 GMT" } ]
2023-08-15T00:00:00
[ [ "Xiong", "Yiyun", "" ], [ "Dai", "Mengwei", "" ], [ "Li", "Fei", "" ], [ "Fei", "Hao", "" ], [ "Li", "Bobo", "" ], [ "Wu", "Shengqiong", "" ], [ "Ji", "Donghong", "" ], [ "Teng", "Chong", "" ] ]
new_dataset
0.999485
2308.04889
Steffen Eger
Steffen Eger and Christoph Leiter and Jonas Belouadi and Ran Zhang and Aida Kostikova and Daniil Larionov and Yanran Chen and Vivian Fresen
NLLG Quarterly arXiv Report 06/23: What are the most influential current AI Papers?
Technical Report
null
null
null
cs.CY cs.AI cs.CL cs.DL cs.LG
http://creativecommons.org/licenses/by-sa/4.0/
The rapid growth of information in the field of Generative Artificial Intelligence (AI), particularly in the subfields of Natural Language Processing (NLP) and Machine Learning (ML), presents a significant challenge for researchers and practitioners to keep pace with the latest developments. To address the problem of information overload, this report by the Natural Language Learning Group at Bielefeld University focuses on identifying the most popular papers on arXiv, with a specific emphasis on NLP and ML. The objective is to offer a quick guide to the most relevant and widely discussed research, aiding both newcomers and established researchers in staying abreast of current trends. In particular, we compile a list of the 40 most popular papers based on normalized citation counts from the first half of 2023. We observe the dominance of papers related to Large Language Models (LLMs) and specifically ChatGPT during the first half of 2023, with the latter showing signs of declining popularity more recently, however. Further, NLP related papers are the most influential (around 60\% of top papers) even though there are twice as many ML related papers in our data. Core issues investigated in the most heavily cited papers are: LLM efficiency, evaluation techniques, ethical considerations, embodied agents, and problem-solving with LLMs. Additionally, we examine the characteristics of top papers in comparison to others outside the top-40 list (noticing the top paper's focus on LLM related issues and higher number of co-authors) and analyze the citation distributions in our dataset, among others.
[ { "version": "v1", "created": "Mon, 31 Jul 2023 11:53:52 GMT" } ]
2023-08-15T00:00:00
[ [ "Eger", "Steffen", "" ], [ "Leiter", "Christoph", "" ], [ "Belouadi", "Jonas", "" ], [ "Zhang", "Ran", "" ], [ "Kostikova", "Aida", "" ], [ "Larionov", "Daniil", "" ], [ "Chen", "Yanran", "" ], [ "Fresen", "Vivian", "" ] ]
new_dataset
0.994675
2308.04890
Jung Ho Ahn
Sangpyo Kim and Jongmin Kim and Jaeyoung Choi and Jung Ho Ahn
CiFHER: A Chiplet-Based FHE Accelerator with a Resizable Structure
15 pages, 9 figures
null
null
null
cs.AR cs.CR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Fully homomorphic encryption (FHE) is in the spotlight as a definitive solution for privacy, but the high computational overhead of FHE poses a challenge to its practical adoption. Although prior studies have attempted to design ASIC accelerators to mitigate the overhead, their designs require excessive amounts of chip resources (e.g., areas) to contain and process massive data for FHE operations. We propose CiFHER, a chiplet-based FHE accelerator with a resizable structure, to tackle the challenge with a cost-effective multi-chip module (MCM) design. First, we devise a flexible architecture of a chiplet core whose configuration can be adjusted to conform to the global organization of chiplets and design constraints. The distinctive feature of our core is a recomposable functional unit providing varying computational throughput for number-theoretic transform (NTT), the most dominant function in FHE. Then, we establish generalized data mapping methodologies to minimize the network overhead when organizing the chips into the MCM package in a tiled manner, which becomes a significant bottleneck due to the technology constraints of MCMs. Also, we analyze the effectiveness of various algorithms, including a novel limb duplication algorithm, on the MCM architecture. A detailed evaluation shows that a CiFHER package composed of 4 to 64 compact chiplets provides performance comparable to state-of-the-art monolithic ASIC FHE accelerators with significantly lower package-wide power consumption while reducing the area of a single core to as small as 4.28mm$^2$.
[ { "version": "v1", "created": "Wed, 9 Aug 2023 11:41:56 GMT" }, { "version": "v2", "created": "Sat, 12 Aug 2023 13:43:33 GMT" } ]
2023-08-15T00:00:00
[ [ "Kim", "Sangpyo", "" ], [ "Kim", "Jongmin", "" ], [ "Choi", "Jaeyoung", "" ], [ "Ahn", "Jung Ho", "" ] ]
new_dataset
0.996048
2308.05667
Zheng Qin
Minhao Li, Zheng Qin, Zhirui Gao, Renjiao Yi, Chenyang Zhu, Yulan Guo, Kai Xu
2D3D-MATR: 2D-3D Matching Transformer for Detection-free Registration between Images and Point Clouds
Accepted by ICCV 2023
null
null
null
cs.CV
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The commonly adopted detect-then-match approach to registration finds difficulties in the cross-modality cases due to the incompatible keypoint detection and inconsistent feature description. We propose, 2D3D-MATR, a detection-free method for accurate and robust registration between images and point clouds. Our method adopts a coarse-to-fine pipeline where it first computes coarse correspondences between downsampled patches of the input image and the point cloud and then extends them to form dense correspondences between pixels and points within the patch region. The coarse-level patch matching is based on transformer which jointly learns global contextual constraints with self-attention and cross-modality correlations with cross-attention. To resolve the scale ambiguity in patch matching, we construct a multi-scale pyramid for each image patch and learn to find for each point patch the best matching image patch at a proper resolution level. Extensive experiments on two public benchmarks demonstrate that 2D3D-MATR outperforms the previous state-of-the-art P2-Net by around $20$ percentage points on inlier ratio and over $10$ points on registration recall. Our code and models are available at https://github.com/minhaolee/2D3DMATR.
[ { "version": "v1", "created": "Thu, 10 Aug 2023 16:10:54 GMT" }, { "version": "v2", "created": "Mon, 14 Aug 2023 12:49:28 GMT" } ]
2023-08-15T00:00:00
[ [ "Li", "Minhao", "" ], [ "Qin", "Zheng", "" ], [ "Gao", "Zhirui", "" ], [ "Yi", "Renjiao", "" ], [ "Zhu", "Chenyang", "" ], [ "Guo", "Yulan", "" ], [ "Xu", "Kai", "" ] ]
new_dataset
0.997968
2308.06358
Yumeng Xue
Luyu Cheng, Bairui Su, Yumeng Xue, Xiaoyu Liu, Yunhai Wang
CA2: Cyber Attacks Analytics
IEEE Conference on Visual Analytics Science and Technology (VAST) Challenge Workshop 2020
null
null
null
cs.HC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The VAST Challenge 2020 Mini-Challenge 1 requires participants to identify the responsible white hat groups behind a fictional Internet outage. To address this task, we have created a visual analytics system named CA2: Cyber Attacks Analytics. This system is designed to efficiently compare and match subgraphs within an extensive graph containing anonymized profiles. Additionally, we showcase an iterative workflow that utilizes our system's capabilities to pinpoint the responsible group.
[ { "version": "v1", "created": "Fri, 11 Aug 2023 19:27:45 GMT" } ]
2023-08-15T00:00:00
[ [ "Cheng", "Luyu", "" ], [ "Su", "Bairui", "" ], [ "Xue", "Yumeng", "" ], [ "Liu", "Xiaoyu", "" ], [ "Wang", "Yunhai", "" ] ]
new_dataset
0.99871
2308.06375
Jiwoong Im
Daniel Jiwoong Im, Alexander Kondratskiy, Vincent Harvey, Hsuan-Wei Fu
UAMM: UBET Automated Market Maker
null
null
null
null
cs.LG cs.CE q-fin.CP
http://creativecommons.org/publicdomain/zero/1.0/
Automated market makers (AMMs) are pricing mechanisms utilized by decentralized exchanges (DEX). Traditional AMM approaches are constrained by pricing solely based on their own liquidity pool, without consideration of external markets or risk management for liquidity providers. In this paper, we propose a new approach known as UBET AMM (UAMM), which calculates prices by considering external market prices and the impermanent loss of the liquidity pool. Despite relying on external market prices, our method maintains the desired properties of a constant product curve when computing slippages. The key element of UAMM is determining the appropriate slippage amount based on the desired target balance, which encourages the liquidity pool to minimize impermanent loss. We demonstrate that our approach eliminates arbitrage opportunities when external market prices are efficient.
[ { "version": "v1", "created": "Fri, 11 Aug 2023 20:17:22 GMT" } ]
2023-08-15T00:00:00
[ [ "Im", "Daniel Jiwoong", "" ], [ "Kondratskiy", "Alexander", "" ], [ "Harvey", "Vincent", "" ], [ "Fu", "Hsuan-Wei", "" ] ]
new_dataset
0.971157
2308.06383
Yan Di
Yan Di, Chenyangguang Zhang, Ruida Zhang, Fabian Manhardt, Yongzhi Su, Jason Rambach, Didier Stricker, Xiangyang Ji and Federico Tombari
U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds
ICCV2023
null
null
null
cs.CV
http://creativecommons.org/licenses/by-nc-sa/4.0/
In this paper, we propose U-RED, an Unsupervised shape REtrieval and Deformation pipeline that takes an arbitrary object observation as input, typically captured by RGB images or scans, and jointly retrieves and deforms the geometrically similar CAD models from a pre-established database to tightly match the target. Considering existing methods typically fail to handle noisy partial observations, U-RED is designed to address this issue from two aspects. First, since one partial shape may correspond to multiple potential full shapes, the retrieval method must allow such an ambiguous one-to-many relationship. Thereby U-RED learns to project all possible full shapes of a partial target onto the surface of a unit sphere. Then during inference, each sampling on the sphere will yield a feasible retrieval. Second, since real-world partial observations usually contain noticeable noise, a reliable learned metric that measures the similarity between shapes is necessary for stable retrieval. In U-RED, we design a novel point-wise residual-guided metric that allows noise-robust comparison. Extensive experiments on the synthetic datasets PartNet, ComplementMe and the real-world dataset Scan2CAD demonstrate that U-RED surpasses existing state-of-the-art approaches by 47.3%, 16.7% and 31.6% respectively under Chamfer Distance.
[ { "version": "v1", "created": "Fri, 11 Aug 2023 20:56:05 GMT" } ]
2023-08-15T00:00:00
[ [ "Di", "Yan", "" ], [ "Zhang", "Chenyangguang", "" ], [ "Zhang", "Ruida", "" ], [ "Manhardt", "Fabian", "" ], [ "Su", "Yongzhi", "" ], [ "Rambach", "Jason", "" ], [ "Stricker", "Didier", "" ], [ "Ji", "Xiangyang", "" ], [ "Tombari", "Federico", "" ] ]
new_dataset
0.987771
2308.06393
Adnan Qayyum
Muhammad Atif Butt, Hassan Ali, Adnan Qayyum, Waqas Sultani, Ala Al-Fuqaha, Junaid Qadir
R2S100K: Road-Region Segmentation Dataset For Semi-Supervised Autonomous Driving in the Wild
null
null
null
null
cs.CV
http://creativecommons.org/licenses/by/4.0/
Semantic understanding of roadways is a key enabling factor for safe autonomous driving. However, existing autonomous driving datasets provide well-structured urban roads while ignoring unstructured roadways containing distress, potholes, water puddles, and various kinds of road patches i.e., earthen, gravel etc. To this end, we introduce Road Region Segmentation dataset (R2S100K) -- a large-scale dataset and benchmark for training and evaluation of road segmentation in aforementioned challenging unstructured roadways. R2S100K comprises 100K images extracted from a large and diverse set of video sequences covering more than 1000 KM of roadways. Out of these 100K privacy respecting images, 14,000 images have fine pixel-labeling of road regions, with 86,000 unlabeled images that can be leveraged through semi-supervised learning methods. Alongside, we present an Efficient Data Sampling (EDS) based self-training framework to improve learning by leveraging unlabeled data. Our experimental results demonstrate that the proposed method significantly improves learning methods in generalizability and reduces the labeling cost for semantic segmentation tasks. Our benchmark will be publicly available to facilitate future research at https://r2s100k.github.io/.
[ { "version": "v1", "created": "Fri, 11 Aug 2023 21:31:37 GMT" } ]
2023-08-15T00:00:00
[ [ "Butt", "Muhammad Atif", "" ], [ "Ali", "Hassan", "" ], [ "Qayyum", "Adnan", "" ], [ "Sultani", "Waqas", "" ], [ "Al-Fuqaha", "Ala", "" ], [ "Qadir", "Junaid", "" ] ]
new_dataset
0.999873
2308.06401
Mohamed Elmahallawy
Yasmine Mustafa, Mohamed Elmahallawy, Tie Luo, Seif Eldawlatly
A Brain-Computer Interface Augmented Reality Framework with Auto-Adaptive SSVEP Recognition
null
null
null
null
cs.HC cs.AI
http://creativecommons.org/licenses/by/4.0/
Brain-Computer Interface (BCI) initially gained attention for developing applications that aid physically impaired individuals. Recently, the idea of integrating BCI with Augmented Reality (AR) emerged, which uses BCI not only to enhance the quality of life for individuals with disabilities but also to develop mainstream applications for healthy users. One commonly used BCI signal pattern is the Steady-state Visually-evoked Potential (SSVEP), which captures the brain's response to flickering visual stimuli. SSVEP-based BCI-AR applications enable users to express their needs/wants by simply looking at corresponding command options. However, individuals are different in brain signals and thus require per-subject SSVEP recognition. Moreover, muscle movements and eye blinks interfere with brain signals, and thus subjects are required to remain still during BCI experiments, which limits AR engagement. In this paper, we (1) propose a simple adaptive ensemble classification system that handles the inter-subject variability, (2) present a simple BCI-AR framework that supports the development of a wide range of SSVEP-based BCI-AR applications, and (3) evaluate the performance of our ensemble algorithm in an SSVEP-based BCI-AR application with head rotations which has demonstrated robustness to the movement interference. Our testing on multiple subjects achieved a mean accuracy of 80\% on a PC and 77\% using the HoloLens AR headset, both of which surpass previous studies that incorporate individual classifiers and head movements. In addition, our visual stimulation time is 5 seconds which is relatively short. The statistically significant results show that our ensemble classification approach outperforms individual classifiers in SSVEP-based BCIs.
[ { "version": "v1", "created": "Fri, 11 Aug 2023 21:56:00 GMT" } ]
2023-08-15T00:00:00
[ [ "Mustafa", "Yasmine", "" ], [ "Elmahallawy", "Mohamed", "" ], [ "Luo", "Tie", "" ], [ "Eldawlatly", "Seif", "" ] ]
new_dataset
0.967206
2308.06445
AKM Mubashwir Alam
AKM Mubashwir Alam, Justin Boyce, Keke Chen
SGX-MR-Prot: Efficient and Developer-Friendly Access-Pattern Protection in Trusted Execution Environments
arXiv admin note: text overlap with arXiv:2009.03518
International Conference on Distributed Computing Systems (ICDCS) 2023
null
null
cs.CR
http://creativecommons.org/licenses/by/4.0/
Trusted Execution Environments, such as Intel SGX, use hardware supports to ensure the confidentiality and integrity of applications against a compromised cloud system. However, side channels like access patterns remain for adversaries to exploit and obtain sensitive information. Common approaches use oblivious programs or primitives, such as ORAM, to make access patterns oblivious to input data, which are challenging to develop. This demonstration shows a prototype SGX-MR-Prot for efficiently protecting access patterns of SGX-based data-intensive applications and minimizing developers' efforts. SGX-MR-Prot uses the MapReduce framework to regulate application dataflows to reduce the cost of access-pattern protection and hide the data oblivious details from SGX developers. This demonstration will allow users to intuitively understand the unique contributions of the framework-based protection approach via interactive exploration and visualization.
[ { "version": "v1", "created": "Sat, 12 Aug 2023 02:44:15 GMT" } ]
2023-08-15T00:00:00
[ [ "Alam", "AKM Mubashwir", "" ], [ "Boyce", "Justin", "" ], [ "Chen", "Keke", "" ] ]
new_dataset
0.960091
2308.06466
Naresh Goud Boddu
Naresh Goud Boddu, Vipul Goyal, Rahul Jain, Jo\~ao Ribeiro
Split-State Non-Malleable Codes and Secret Sharing Schemes for Quantum Messages
null
null
null
null
cs.CR quant-ph
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Non-malleable codes are fundamental objects at the intersection of cryptography and coding theory. These codes provide security guarantees even in settings where error correction and detection are impossible, and have found applications to several other cryptographic tasks. Roughly speaking, a non-malleable code for a family of tampering functions guarantees that no adversary can tamper (using functions from this family) the encoding of a given message into the encoding of a related distinct message. Non-malleable secret sharing schemes are a strengthening of non-malleable codes which satisfy additional privacy and reconstruction properties. We first focus on the $2$-split-state tampering model, one of the strongest and most well-studied adversarial tampering models. Here, a codeword is split into two parts which are stored in physically distant servers, and the adversary can then independently tamper with each part using arbitrary functions. This model can be naturally extended to the secret sharing setting with several parties by having the adversary independently tamper with each share. Previous works on non-malleable coding and secret sharing in the split-state tampering model only considered the encoding of \emph{classical} messages. Furthermore, until the recent work by Aggarwal, Boddu, and Jain (arXiv 2022), adversaries with quantum capabilities and \emph{shared entanglement} had not been considered, and it is a priori not clear whether previous schemes remain secure in this model. In this work, we introduce the notions of split-state non-malleable codes and secret sharing schemes for quantum messages secure against quantum adversaries with shared entanglement. We also present explicit constructions of such schemes that achieve low-error non-malleability.
[ { "version": "v1", "created": "Sat, 12 Aug 2023 05:15:35 GMT" } ]
2023-08-15T00:00:00
[ [ "Boddu", "Naresh Goud", "" ], [ "Goyal", "Vipul", "" ], [ "Jain", "Rahul", "" ], [ "Ribeiro", "João", "" ] ]
new_dataset
0.997619
2308.06479
Jia Zhang
Jia Zhang, Xin Na, Rui Xi, Yimiao Sun, Yuan He
mmHawkeye: Passive UAV Detection with a COTS mmWave Radar
9 pages, 14 figures, IEEE SECON2023
null
null
null
cs.NI eess.SP
http://creativecommons.org/licenses/by/4.0/
Small Unmanned Aerial Vehicles (UAVs) are becoming potential threats to security-sensitive areas and personal privacy. A UAV can shoot photos at height, but how to detect such an uninvited intruder is an open problem. This paper presents mmHawkeye, a passive approach for UAV detection with a COTS millimeter wave (mmWave) radar. mmHawkeye doesn't require prior knowledge of the type, motions, and flight trajectory of the UAV, while exploiting the signal feature induced by the UAV's periodic micro-motion (PMM) for long-range accurate detection. The design is therefore effective in dealing with low-SNR and uncertain reflected signals from the UAV. mmHawkeye can further track the UAV's position with dynamic programming and particle filtering, and identify it with a Long Short-Term Memory (LSTM) based detector. We implement mmHawkeye on a commercial mmWave radar and evaluate its performance under varied settings. The experimental results show that mmHawkeye has a detection accuracy of 95.8% and can realize detection at a range up to 80m.
[ { "version": "v1", "created": "Sat, 12 Aug 2023 06:14:15 GMT" } ]
2023-08-15T00:00:00
[ [ "Zhang", "Jia", "" ], [ "Na", "Xin", "" ], [ "Xi", "Rui", "" ], [ "Sun", "Yimiao", "" ], [ "He", "Yuan", "" ] ]
new_dataset
0.993869
2308.06483
Yenan Zhang
Yenan Zhang and Hiroshi Watanabe
BigWavGAN: A Wave-To-Wave Generative Adversarial Network for Music Super-Resolution
null
null
null
null
cs.SD eess.AS
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Generally, Deep Neural Networks (DNNs) are expected to have high performance when their model size is large. However, large models failed to produce high-quality results commensurate with their scale in music Super-Resolution (SR). We attribute this to that DNNs cannot learn information commensurate with their size from standard mean square error losses. To unleash the potential of large DNN models in music SR, we propose BigWavGAN, which incorporates Demucs, a large-scale wave-to-wave model, with State-Of-The-Art (SOTA) discriminators and adversarial training strategies. Our discriminator consists of Multi-Scale Discriminator (MSD) and Multi-Resolution Discriminator (MRD). During inference, since only the generator is utilized, there are no additional parameters or computational resources required compared to the baseline model Demucs. Objective evaluation affirms the effectiveness of BigWavGAN in music SR. Subjective evaluations indicate that BigWavGAN can generate music with significantly high perceptual quality over the baseline model. Notably, BigWavGAN surpasses the SOTA music SR model in both simulated and real-world scenarios. Moreover, BigWavGAN represents its superior generalization ability to address out-of-distribution data. The conducted ablation study reveals the importance of our discriminators and training strategies. Samples are available on the demo page.
[ { "version": "v1", "created": "Sat, 12 Aug 2023 06:40:46 GMT" } ]
2023-08-15T00:00:00
[ [ "Zhang", "Yenan", "" ], [ "Watanabe", "Hiroshi", "" ] ]
new_dataset
0.997822
2308.06488
Tahsina Hashem
Tahsina Hashem, Weiqing Wang, Derry Tanti Wijaya, Mohammed Eunus Ali, Yuan-Fang Li
Generating Faithful Text From a Knowledge Graph with Noisy Reference Text
null
null
null
null
cs.CL cs.AI
http://creativecommons.org/licenses/by/4.0/
Knowledge Graph (KG)-to-Text generation aims at generating fluent natural-language text that accurately represents the information of a given knowledge graph. While significant progress has been made in this task by exploiting the power of pre-trained language models (PLMs) with appropriate graph structure-aware modules, existing models still fall short of generating faithful text, especially when the ground-truth natural-language text contains additional information that is not present in the graph. In this paper, we develop a KG-to-text generation model that can generate faithful natural-language text from a given graph, in the presence of noisy reference text. Our framework incorporates two core ideas: Firstly, we utilize contrastive learning to enhance the model's ability to differentiate between faithful and hallucinated information in the text, thereby encouraging the decoder to generate text that aligns with the input graph. Secondly, we empower the decoder to control the level of hallucination in the generated text by employing a controllable text generation technique. We evaluate our model's performance through the standard quantitative metrics as well as a ChatGPT-based quantitative and qualitative analysis. Our evaluation demonstrates the superior performance of our model over state-of-the-art KG-to-text models on faithfulness.
[ { "version": "v1", "created": "Sat, 12 Aug 2023 07:12:45 GMT" } ]
2023-08-15T00:00:00
[ [ "Hashem", "Tahsina", "" ], [ "Wang", "Weiqing", "" ], [ "Wijaya", "Derry Tanti", "" ], [ "Ali", "Mohammed Eunus", "" ], [ "Li", "Yuan-Fang", "" ] ]
new_dataset
0.980779
2308.06549
Tanvir Islam
Tanvir Islam, Anika Rahman Joyita, Md. Golam Rabiul Alam, Mohammad Mehedi Hassan, Md. Rafiul Hassan, Raffaele Gravina
Human Behavior-based Personalized Meal Recommendation and Menu Planning Social System
null
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS. 2022
null
null
cs.HC cs.LG
http://creativecommons.org/licenses/by/4.0/
The traditional dietary recommendation systems are basically nutrition or health-aware where the human feelings on food are ignored. Human affects vary when it comes to food cravings, and not all foods are appealing in all moods. A questionnaire-based and preference-aware meal recommendation system can be a solution. However, automated recognition of social affects on different foods and planning the menu considering nutritional demand and social-affect has some significant benefits of the questionnaire-based and preference-aware meal recommendations. A patient with severe illness, a person in a coma, or patients with locked-in syndrome and amyotrophic lateral sclerosis (ALS) cannot express their meal preferences. Therefore, the proposed framework includes a social-affective computing module to recognize the affects of different meals where the person's affect is detected using electroencephalography signals. EEG allows to capture the brain signals and analyze them to anticipate affective toward a food. In this study, we have used a 14-channel wireless Emotive Epoc+ to measure affectivity for different food items. A hierarchical ensemble method is applied to predict affectivity upon multiple feature extraction methods and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is used to generate a food list based on the predicted affectivity. In addition to the meal recommendation, an automated menu planning approach is also proposed considering a person's energy intake requirement, affectivity, and nutritional values of the different menus. The bin-packing algorithm is used for the personalized menu planning of breakfast, lunch, dinner, and snacks. The experimental findings reveal that the suggested affective computing, meal recommendation, and menu planning algorithms perform well across a variety of assessment parameters.
[ { "version": "v1", "created": "Sat, 12 Aug 2023 12:19:23 GMT" } ]
2023-08-15T00:00:00
[ [ "Islam", "Tanvir", "" ], [ "Joyita", "Anika Rahman", "" ], [ "Alam", "Md. Golam Rabiul", "" ], [ "Hassan", "Mohammad Mehedi", "" ], [ "Hassan", "Md. Rafiul", "" ], [ "Gravina", "Raffaele", "" ] ]
new_dataset
0.971047
2308.06568
Hanna Halaburda
Joshua S. Gans and Hanna Halaburda
"Zero Cost'' Majority Attacks on Permissionless Blockchains
null
null
null
null
cs.CR cs.GT econ.GN q-fin.EC
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
The core premise of permissionless blockchains is their reliable and secure operation without the need to trust any individual agent. At the heart of blockchain consensus mechanisms is an explicit cost (whether work or stake) for participation in the network and the opportunity to add blocks to the blockchain. A key rationale for that cost is to make attacks on the network, which could be theoretically carried out if a majority of nodes were controlled by a single entity, too expensive to be worthwhile. We demonstrate that a majority attacker can successfully attack with a {\em negative cost}, which shows that the protocol mechanisms are insufficient to create a secure network, and emphasizes the importance of socially driven mechanisms external to the protocol. At the same time, negative cost enables a new type of majority attack that is more likely to elude external scrutiny.
[ { "version": "v1", "created": "Sat, 12 Aug 2023 13:38:37 GMT" } ]
2023-08-15T00:00:00
[ [ "Gans", "Joshua S.", "" ], [ "Halaburda", "Hanna", "" ] ]
new_dataset
0.989411
2308.06571
Hangjie Yuan
Jiuniu Wang, Hangjie Yuan, Dayou Chen, Yingya Zhang, Xiang Wang, Shiwei Zhang
ModelScope Text-to-Video Technical Report
Technical report. Project page: \url{https://modelscope.cn/models/damo/text-to-video-synthesis/summary}
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
This paper introduces ModelScopeT2V, a text-to-video synthesis model that evolves from a text-to-image synthesis model (i.e., Stable Diffusion). ModelScopeT2V incorporates spatio-temporal blocks to ensure consistent frame generation and smooth movement transitions. The model could adapt to varying frame numbers during training and inference, rendering it suitable for both image-text and video-text datasets. ModelScopeT2V brings together three components (i.e., VQGAN, a text encoder, and a denoising UNet), totally comprising 1.7 billion parameters, in which 0.5 billion parameters are dedicated to temporal capabilities. The model demonstrates superior performance over state-of-the-art methods across three evaluation metrics. The code and an online demo are available at \url{https://modelscope.cn/models/damo/text-to-video-synthesis/summary}.
[ { "version": "v1", "created": "Sat, 12 Aug 2023 13:53:10 GMT" } ]
2023-08-15T00:00:00
[ [ "Wang", "Jiuniu", "" ], [ "Yuan", "Hangjie", "" ], [ "Chen", "Dayou", "" ], [ "Zhang", "Yingya", "" ], [ "Wang", "Xiang", "" ], [ "Zhang", "Shiwei", "" ] ]
new_dataset
0.999234
2308.06573
Shouyi Lu
Guirong Zhuo, Shouyi Lu, Huanyu Zhou, Lianqing Zheng, Lu Xiong
4DRVO-Net: Deep 4D Radar-Visual Odometry Using Multi-Modal and Multi-Scale Adaptive Fusion
14 pages,12 figures
null
null
null
cs.CV cs.AI
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Four-dimensional (4D) radar--visual odometry (4DRVO) integrates complementary information from 4D radar and cameras, making it an attractive solution for achieving accurate and robust pose estimation. However, 4DRVO may exhibit significant tracking errors owing to three main factors: 1) sparsity of 4D radar point clouds; 2) inaccurate data association and insufficient feature interaction between the 4D radar and camera; and 3) disturbances caused by dynamic objects in the environment, affecting odometry estimation. In this paper, we present 4DRVO-Net, which is a method for 4D radar--visual odometry. This method leverages the feature pyramid, pose warping, and cost volume (PWC) network architecture to progressively estimate and refine poses. Specifically, we propose a multi-scale feature extraction network called Radar-PointNet++ that fully considers rich 4D radar point information, enabling fine-grained learning for sparse 4D radar point clouds. To effectively integrate the two modalities, we design an adaptive 4D radar--camera fusion module (A-RCFM) that automatically selects image features based on 4D radar point features, facilitating multi-scale cross-modal feature interaction and adaptive multi-modal feature fusion. In addition, we introduce a velocity-guided point-confidence estimation module to measure local motion patterns, reduce the influence of dynamic objects and outliers, and provide continuous updates during pose refinement. We demonstrate the excellent performance of our method and the effectiveness of each module design on both the VoD and in-house datasets. Our method outperforms all learning-based and geometry-based methods for most sequences in the VoD dataset. Furthermore, it has exhibited promising performance that closely approaches that of the 64-line LiDAR odometry results of A-LOAM without mapping optimization.
[ { "version": "v1", "created": "Sat, 12 Aug 2023 14:00:09 GMT" } ]
2023-08-15T00:00:00
[ [ "Zhuo", "Guirong", "" ], [ "Lu", "Shouyi", "" ], [ "Zhou", "Huanyu", "" ], [ "Zheng", "Lianqing", "" ], [ "Xiong", "Lu", "" ] ]
new_dataset
0.994325
2308.06594
Jumman Hossain
Jumman Hossain, Abu-Zaher Faridee, Nirmalya Roy, Anjan Basak, Derrik E. Asher
CoverNav: Cover Following Navigation Planning in Unstructured Outdoor Environment with Deep Reinforcement Learning
null
null
null
null
cs.RO cs.LG
http://creativecommons.org/licenses/by/4.0/
Autonomous navigation in offroad environments has been extensively studied in the robotics field. However, navigation in covert situations where an autonomous vehicle needs to remain hidden from outside observers remains an underexplored area. In this paper, we propose a novel Deep Reinforcement Learning (DRL) based algorithm, called CoverNav, for identifying covert and navigable trajectories with minimal cost in offroad terrains and jungle environments in the presence of observers. CoverNav focuses on unmanned ground vehicles seeking shelters and taking covers while safely navigating to a predefined destination. Our proposed DRL method computes a local cost map that helps distinguish which path will grant the maximal covertness while maintaining a low cost trajectory using an elevation map generated from 3D point cloud data, the robot's pose, and directed goal information. CoverNav helps robot agents to learn the low elevation terrain using a reward function while penalizing it proportionately when it experiences high elevation. If an observer is spotted, CoverNav enables the robot to select natural obstacles (e.g., rocks, houses, disabled vehicles, trees, etc.) and use them as shelters to hide behind. We evaluate CoverNav using the Unity simulation environment and show that it guarantees dynamically feasible velocities in the terrain when fed with an elevation map generated by another DRL based navigation algorithm. Additionally, we evaluate CoverNav's effectiveness in achieving a maximum goal distance of 12 meters and its success rate in different elevation scenarios with and without cover objects. We observe competitive performance comparable to state of the art (SOTA) methods without compromising accuracy.
[ { "version": "v1", "created": "Sat, 12 Aug 2023 15:19:49 GMT" } ]
2023-08-15T00:00:00
[ [ "Hossain", "Jumman", "" ], [ "Faridee", "Abu-Zaher", "" ], [ "Roy", "Nirmalya", "" ], [ "Basak", "Anjan", "" ], [ "Asher", "Derrik E.", "" ] ]
new_dataset
0.991634
2308.06639
Huaishu Peng
Zeyu Yan, Hsuanling Lee, Liang He, Huaishu Peng
3D Printing Magnetophoretic Displays
null
UIST 2023
10.1145/3586183.3606804
null
cs.HC cs.GR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
We present a pipeline for printing interactive and always-on magnetophoretic displays using affordable Fused Deposition Modeling (FDM) 3D printers. Using our pipeline, an end-user can convert the surface of a 3D shape into a matrix of voxels. The generated model can be sent to an FDM 3D printer equipped with an additional syringe-based injector. During the printing process, an oil and iron powder-based liquid mixture is injected into each voxel cell, allowing the appearance of the once-printed object to be editable with external magnetic sources. To achieve this, we made modifications to the 3D printer hardware and the firmware. We also developed a 3D editor to prepare printable models. We demonstrate our pipeline with a variety of examples, including a printed Stanford bunny with customizable appearances, a small espresso mug that can be used as a post-it note surface, a board game figurine with a computationally updated display, and a collection of flexible wearable accessories with editable visuals.
[ { "version": "v1", "created": "Sat, 12 Aug 2023 20:07:18 GMT" } ]
2023-08-15T00:00:00
[ [ "Yan", "Zeyu", "" ], [ "Lee", "Hsuanling", "" ], [ "He", "Liang", "" ], [ "Peng", "Huaishu", "" ] ]
new_dataset
0.996419
2308.06680
Noman Bashir
Diptyaroop Maji, Noman Bashir, David Irwin, Prashant Shenoy, Ramesh K. Sitaraman
Untangling Carbon-free Energy Attribution and Carbon Intensity Estimation for Carbon-aware Computing
null
null
null
null
cs.DC
http://creativecommons.org/licenses/by/4.0/
Many organizations, including governments, utilities, and businesses, have set ambitious targets to reduce carbon emissions as a part of their sustainability goals. To achieve these targets, these organizations increasingly use power purchase agreements (PPAs) to obtain renewable energy credits, which they use to offset their ``brown'' energy consumption. However, the details of these PPAs are often private and not shared with important stakeholders, such as grid operators and carbon information services, who monitor and report the grid's carbon emissions. This often results in incorrect carbon accounting where the same renewable energy production could be factored into grid carbon emission reports and also separately claimed by organizations that own PPAs. Such ``double counting'' of renewable energy production could lead to organizations with PPAs to understate their carbon emissions and overstate their progress towards their sustainability goals. Further, we show that commonly-used carbon reduction measures, such as load shifting, can have the opposite effect of increasing emissions if such measures were to use inaccurate carbon intensity signals. For instance, users may increase energy consumption because the grid's carbon intensity appears low even though carbon intensity may actually be high when renewable energy attributed to PPAs are excluded. Unfortunately, there is currently no consensus on how to accurately compute the grid's carbon intensity by properly accounting for PPAs. The goal of our work is to shed quantitative light on the renewable energy attribution problem and evaluate its impact of inaccurate accounting on carbon-aware systems.
[ { "version": "v1", "created": "Sun, 13 Aug 2023 04:02:15 GMT" } ]
2023-08-15T00:00:00
[ [ "Maji", "Diptyaroop", "" ], [ "Bashir", "Noman", "" ], [ "Irwin", "David", "" ], [ "Shenoy", "Prashant", "" ], [ "Sitaraman", "Ramesh K.", "" ] ]
new_dataset
0.976395
2308.06687
Shibsankar Das
Shibsankar Das, Adrish Banerjee, and Zilong Liu
Root Cross Z-Complementary Pairs with Large ZCZ Width
This work has been presented in 2022 IEEE International Symposium on Information Theory (ISIT), Espoo, Finland
2022 IEEE International Symposium on Information Theory (ISIT), Espoo, Finland, 2022, pp. 522-527
10.1109/ISIT50566.2022.9834651
null
cs.IT math.IT
http://creativecommons.org/licenses/by/4.0/
In this paper, we present a new family of cross $Z$-complementary pairs (CZCPs) based on generalized Boolean functions and two roots of unity. Our key idea is to consider an arbitrary partition of the set $\{1,2,\cdots, n\}$ with two subsets corresponding to two given roots of unity for which two truncated sequences of new alphabet size determined by the two roots of unity are obtained. We show that these two truncated sequences form a new $q$-ary CZCP with flexible sequence length and large zero-correlation zone width. Furthermore, we derive an enumeration formula by considering the Stirling number of the second kind for the partitions and show that the number of constructed CZCPs increases significantly compared to the existing works.
[ { "version": "v1", "created": "Sun, 13 Aug 2023 05:27:15 GMT" } ]
2023-08-15T00:00:00
[ [ "Das", "Shibsankar", "" ], [ "Banerjee", "Adrish", "" ], [ "Liu", "Zilong", "" ] ]
new_dataset
0.990901
2308.06690
Shibsankar Das
Shibsankar Das, Adrish Banerjee, and Udaya Parampalli
Two-Dimensional Z-Complementary Array Quads with Low Column Sequence PMEPRs
This work has been presented in 2023 IEEE International Symposium on Information Theory (ISIT), Taipei, Taiwan
null
null
null
cs.IT math.IT
http://creativecommons.org/licenses/by/4.0/
In this paper, we first propose a new design strategy of 2D $Z$-complementary array quads (2D-ZCAQs) with feasible array sizes. A 2D-ZCAQ consists of four distinct unimodular arrays satisfying zero 2D auto-correlation sums for non-trivial 2D time-shifts within certain zone. Then, we obtain the upper bounds on the column sequence peak-to-mean envelope power ratio (PMEPR) of the constructed 2D-ZCAQs by using specific auto-correlation properties of some seed sequences. The constructed 2D-ZCAQs with bounded column sequence PMEPR can be used as a potential alternative to 2D Golay complementary array sets for practical applications
[ { "version": "v1", "created": "Sun, 13 Aug 2023 05:46:43 GMT" } ]
2023-08-15T00:00:00
[ [ "Das", "Shibsankar", "" ], [ "Banerjee", "Adrish", "" ], [ "Parampalli", "Udaya", "" ] ]
new_dataset
0.99909
2308.06692
Mingkai Zheng
Mingkai Zheng, Shan You, Lang Huang, Chen Luo, Fei Wang, Chen Qian, Chang Xu
SimMatchV2: Semi-Supervised Learning with Graph Consistency
null
null
null
null
cs.CV cs.LG
http://creativecommons.org/licenses/by/4.0/
Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor. In this paper, we introduce a new semi-supervised learning algorithm - SimMatchV2, which formulates various consistency regularizations between labeled and unlabeled data from the graph perspective. In SimMatchV2, we regard the augmented view of a sample as a node, which consists of a label and its corresponding representation. Different nodes are connected with the edges, which are measured by the similarity of the node representations. Inspired by the message passing and node classification in graph theory, we propose four types of consistencies, namely 1) node-node consistency, 2) node-edge consistency, 3) edge-edge consistency, and 4) edge-node consistency. We also uncover that a simple feature normalization can reduce the gaps of the feature norm between different augmented views, significantly improving the performance of SimMatchV2. Our SimMatchV2 has been validated on multiple semi-supervised learning benchmarks. Notably, with ResNet-50 as our backbone and 300 epochs of training, SimMatchV2 achieves 71.9\% and 76.2\% Top-1 Accuracy with 1\% and 10\% labeled examples on ImageNet, which significantly outperforms the previous methods and achieves state-of-the-art performance. Code and pre-trained models are available at \href{https://github.com/mingkai-zheng/SimMatchV2}{https://github.com/mingkai-zheng/SimMatchV2}.
[ { "version": "v1", "created": "Sun, 13 Aug 2023 05:56:36 GMT" } ]
2023-08-15T00:00:00
[ [ "Zheng", "Mingkai", "" ], [ "You", "Shan", "" ], [ "Huang", "Lang", "" ], [ "Luo", "Chen", "" ], [ "Wang", "Fei", "" ], [ "Qian", "Chen", "" ], [ "Xu", "Chang", "" ] ]
new_dataset
0.990284
2308.06696
Yichi Zhang
Yichi Zhang, Zhuo Chen, Wen Zhang
MACO: A Modality Adversarial and Contrastive Framework for Modality-missing Multi-modal Knowledge Graph Completion
This is the ArXiv version of our paper accepted by NLPCC 2023. The code will be released soon
null
null
null
cs.CL cs.AI cs.MM
http://creativecommons.org/licenses/by-sa/4.0/
Recent years have seen significant advancements in multi-modal knowledge graph completion (MMKGC). MMKGC enhances knowledge graph completion (KGC) by integrating multi-modal entity information, thereby facilitating the discovery of unobserved triples in the large-scale knowledge graphs (KGs). Nevertheless, existing methods emphasize the design of elegant KGC models to facilitate modality interaction, neglecting the real-life problem of missing modalities in KGs. The missing modality information impedes modal interaction, consequently undermining the model's performance. In this paper, we propose a modality adversarial and contrastive framework (MACO) to solve the modality-missing problem in MMKGC. MACO trains a generator and discriminator adversarially to generate missing modality features that can be incorporated into the MMKGC model. Meanwhile, we design a cross-modal contrastive loss to improve the performance of the generator. Experiments on public benchmarks with further explorations demonstrate that MACO could achieve state-of-the-art results and serve as a versatile framework to bolster various MMKGC models. Our code and benchmark data are available at https://github.com/zjukg/MACO.
[ { "version": "v1", "created": "Sun, 13 Aug 2023 06:29:38 GMT" } ]
2023-08-15T00:00:00
[ [ "Zhang", "Yichi", "" ], [ "Chen", "Zhuo", "" ], [ "Zhang", "Wen", "" ] ]
new_dataset
0.997823
2308.06699
Jia Li
Jia Li, Ziling Chen, Xiaolong Wu, Lu Wang, Beibei Wang, Lei Zhang
Neural Super-Resolution for Real-time Rendering with Radiance Demodulation
null
null
null
null
cs.GR
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Rendering high-resolution images in real-time applications (e.g., video games, virtual reality) is time-consuming, thus super-resolution technology becomes more and more crucial in real-time rendering. However, it is still challenging to preserve sharp texture details, keep the temporal stability and avoid the ghosting artifacts in the real-time rendering super-resolution. To this end, we introduce radiance demodulation into real-time rendering super-resolution, separating the rendered image or radiance into a lighting component and a material component, due to the fact that the light component tends to be smoother than the rendered image and the high-resolution material component with detailed textures can be easily obtained. Therefore, we perform the super-resolution only on the lighting component and re-modulate with the high-resolution material component to obtain the final super-resolution image. In this way, the texture details can be preserved much better. Then, we propose a reliable warping module by explicitly pointing out the unreliable occluded regions with a motion mask to remove the ghosting artifacts. We further enhance the temporal stability by designing a frame-recurrent neural network to aggregate the previous and current frames, which better captures the spatial-temporal correlation between reconstructed frames. As a result, our method is able to produce temporally stable results in real-time rendering with high-quality details, even in the highly challenging 4 $\times$ 4 super-resolution scenarios.
[ { "version": "v1", "created": "Sun, 13 Aug 2023 06:40:41 GMT" } ]
2023-08-15T00:00:00
[ [ "Li", "Jia", "" ], [ "Chen", "Ziling", "" ], [ "Wu", "Xiaolong", "" ], [ "Wang", "Lu", "" ], [ "Wang", "Beibei", "" ], [ "Zhang", "Lei", "" ] ]
new_dataset
0.987877
2308.06701
Haichao Zhang
Haichao Zhang, Can Qin, Yu Yin, Yun Fu
Camouflaged Image Synthesis Is All You Need to Boost Camouflaged Detection
null
null
null
null
cs.CV cs.AI cs.LG
http://creativecommons.org/licenses/by-nc-nd/4.0/
Camouflaged objects that blend into natural scenes pose significant challenges for deep-learning models to detect and synthesize. While camouflaged object detection is a crucial task in computer vision with diverse real-world applications, this research topic has been constrained by limited data availability. We propose a framework for synthesizing camouflage data to enhance the detection of camouflaged objects in natural scenes. Our approach employs a generative model to produce realistic camouflage images, which can be used to train existing object detection models. Specifically, we use a camouflage environment generator supervised by a camouflage distribution classifier to synthesize the camouflage images, which are then fed into our generator to expand the dataset. Our framework outperforms the current state-of-the-art method on three datasets (COD10k, CAMO, and CHAMELEON), demonstrating its effectiveness in improving camouflaged object detection. This approach can serve as a plug-and-play data generation and augmentation module for existing camouflaged object detection tasks and provides a novel way to introduce more diversity and distributions into current camouflage datasets.
[ { "version": "v1", "created": "Sun, 13 Aug 2023 06:55:05 GMT" } ]
2023-08-15T00:00:00
[ [ "Zhang", "Haichao", "" ], [ "Qin", "Can", "" ], [ "Yin", "Yu", "" ], [ "Fu", "Yun", "" ] ]
new_dataset
0.986921