2021 IEEE/CVF International Conference on Computer Vision (ICCV)最新文献_第10页

DWKS : A Local Descriptor of Deformations Between Meshes and Point Clouds DWKS:网格和点云之间变形的局部描述符

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.00377

Robin Magnet, M. Ovsjanikov

引用次数: 1

Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation 基于模态混淆和中心聚集的跨模态人物再识别

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.01609

Xin Hao, Sanyuan Zhao, Mang Ye, Jianbing Shen

{"title":"Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation","authors":"Xin Hao, Sanyuan Zhao, Mang Ye, Jianbing Shen","doi":"10.1109/ICCV48922.2021.01609","DOIUrl":"https://doi.org/10.1109/ICCV48922.2021.01609","url":null,"abstract":"Cross-modality person re-identification is a challenging task due to large cross-modality discrepancy and intramodality variations. Currently, most existing methods focus on learning modality-specific or modality-shareable features by using the identity supervision or modality label. Different from existing methods, this paper presents a novel Modality Confusion Learning Network (MCLNet). Its basic idea is to confuse two modalities, ensuring that the optimization is explicitly concentrated on the modality-irrelevant perspective. Specifically, MCLNet is designed to learn modality-invariant features by simultaneously minimizing inter-modality discrepancy while maximizing cross-modality similarity among instances in a single framework. Furthermore, an identity-aware marginal center aggregation strategy is introduced to extract the centralization features, while keeping diversity with a marginal constraint. Finally, we design a camera-aware learning scheme to enrich the discriminability. Extensive experiments on SYSU-MM01 and RegDB datasets show that MCLNet outperforms the state-of-the-art by a large margin. On the large-scale SYSU-MM01 dataset, our model can achieve 65.40 % and 61.98 % in terms of Rank-1 accuracy and mAP value.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"35 1","pages":"16383-16392"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85746695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 67

Gait Recognition in the Wild: A Benchmark 野外步态识别:一个基准

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/iccv48922.2021.01452

Zheng Zhu, Xianda Guo, Tian Yang, Junjie Huang, Jiankang Deng, Guan Huang, Dalong Du, Jiwen Lu, Jie Zhou

{"title":"Gait Recognition in the Wild: A Benchmark","authors":"Zheng Zhu, Xianda Guo, Tian Yang, Junjie Huang, Jiankang Deng, Guan Huang, Dalong Du, Jiwen Lu, Jie Zhou","doi":"10.1109/iccv48922.2021.01452","DOIUrl":"https://doi.org/10.1109/iccv48922.2021.01452","url":null,"abstract":"Gait benchmarks empower the research community to train and evaluate high-performance gait recognition systems. Even though growing efforts have been devoted to cross-view recognition, academia is restricted by current existing databases captured in the controlled environment. In this paper, we contribute a new benchmark for Gait REcognition in the Wild (GREW). The GREW dataset is constructed from natural videos, which contains hundreds of cameras and thousands of hours streams in open systems. With tremendous manual annotations, the GREW consists of 26K identities and 128K sequences with rich attributes for unconstrained gait recognition. Moreover, we add a distractor set of over 233K sequences, making it more suitable for real-world applications. Compared with prevailing predefined cross-view datasets, the GREW has diverse and practical view variations, as well as more natural challenging factors. To the best of our knowledge, this is the first large-scale dataset for gait recognition in the wild. Equipped with this benchmark, we dissect the unconstrained gait recognition problem. Representative appearance-based and model-based methods are explored, and comprehensive baselines are established. Experimental results show (1) The proposed GREW benchmark is necessary for training and evaluating gait recognizer in the wild. (2) For state-of-the-art gait recognition approaches, there is a lot of room for improvement. (3) The GREW benchmark can be used as effective pre-training for controlled gait recognition. Benchmark website is https://www.grew-benchmark.org/.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"108 1","pages":"14769-14779"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85797240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 76

Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner? 尺度一致的单目深度能否以自监督尺度不变的方式学习?

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.01249

Lijun Wang, Yifan Wang, Linzhao Wang, Yu-Wei Zhan, Ying Wang, Huchuan Lu

{"title":"Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?","authors":"Lijun Wang, Yifan Wang, Linzhao Wang, Yu-Wei Zhan, Ying Wang, Huchuan Lu","doi":"10.1109/ICCV48922.2021.01249","DOIUrl":"https://doi.org/10.1109/ICCV48922.2021.01249","url":null,"abstract":"Geometric constraints are shown to enforce scale consistency and remedy the scale ambiguity issue in self-supervised monocular depth estimation. Meanwhile, scale-invariant losses focus on learning relative depth, leading to accurate relative depth prediction. To combine the best of both worlds, we learn scale-consistent self-supervised depth in a scale-invariant manner. Towards this goal, we present a scale-aware geometric (SAG) loss, which enforces scale consistency through point cloud alignment. Compared to prior arts, SAG loss takes relative scale into consideration during relative motion estimation, enabling more precise alignment and explicit supervision for scale inference. In addition, a novel two-stream architecture for depth estimation is designed, which disentangles scale from depth estimation and allows depth to be learned in a scale-invariant manner. The integration of SAG loss and two-stream network enables more consistent scale inference and more accurate relative depth estimation. Our method achieves state-of-the-art performance under both scale-invariant and scale-dependent evaluation settings.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"6 1","pages":"12707-12716"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86279441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 25

Defending against Universal Adversarial Patches by Clipping Feature Norms 通过裁剪特征规范防御通用对抗性补丁

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.01612

Cheng Yu, Jiansheng Chen, Youze Xue, Yuyang Liu, Weitao Wan, Jiayu Bao, Huimin Ma

引用次数: 13

PrimitiveNet: Primitive Instance Segmentation with Local Primitive Embedding under Adversarial Metric PrimitiveNet:基于对抗度量的局部基元嵌入基元实例分割

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.01506

Jingwei Huang, Yanfeng Zhang, Mingwei Sun

{"title":"PrimitiveNet: Primitive Instance Segmentation with Local Primitive Embedding under Adversarial Metric","authors":"Jingwei Huang, Yanfeng Zhang, Mingwei Sun","doi":"10.1109/ICCV48922.2021.01506","DOIUrl":"https://doi.org/10.1109/ICCV48922.2021.01506","url":null,"abstract":"We present PrimitiveNet, a novel approach for high-resolution primitive instance segmentation from point clouds on a large scale. Our key idea is to transform the global segmentation problem into easier local tasks. We train a high-resolution primitive embedding network to predict explicit geometry features and implicit latent features for each point. The embedding is jointly trained with an adversarial network as a primitive discriminator to decide whether points are from the same primitive instance in local neighborhoods. Such local supervision encourages the learned embedding and discriminator to describe local surface properties and robustly distinguish different instances. At inference time, network predictions are followed by a region growing method to finalize the segmentation. Experiments show that our method outperforms existing state-of-the-arts based on mean average precision by a significant margin (46.3%) on ABC dataset [31]. We can process extremely large real scenes covering more than 0.1km2. Ablation studies highlight the contribution of our core designs. Finally, our method can improve geometry processing algorithms to abstract scans as lightweight models. Code and data will be available based on Pytorch1 and Mindspore2.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"40 1","pages":"15323-15333"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82250295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

PARTS: Unsupervised segmentation with slots, attention and independence maximization 部分:无监督分割槽，注意力和独立性最大化

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.01027

Daniel Zoran, Rishabh Kabra, Alexander Lerchner, Danilo Jimenez Rezende

{"title":"PARTS: Unsupervised segmentation with slots, attention and independence maximization","authors":"Daniel Zoran, Rishabh Kabra, Alexander Lerchner, Danilo Jimenez Rezende","doi":"10.1109/ICCV48922.2021.01027","DOIUrl":"https://doi.org/10.1109/ICCV48922.2021.01027","url":null,"abstract":"From an early age, humans perceive the visual world as composed of coherent objects with distinctive properties such as shape, size, and color. There is great interest in building models that are able to learn similar structure, ideally in an unsupervised manner. Learning such structure from complex 3D scenes that include clutter, occlusions, interactions, and camera motion is still an open challenge. We present a model that is able to segment visual scenes from complex 3D environments into distinct objects, learn disentangled representations of individual objects, and form consistent and coherent predictions of future frames, in a fully unsupervised manner. Our model (named PARTS) builds on recent approaches that utilize iterative amortized inference and transition dynamics for deep generative models. We achieve dramatic improvements in performance by introducing several novel contributions. We introduce a recurrent slot-attention like encoder which allows for top-down influence during inference. We argue that when inferring scene structure from image sequences it is better to use a fixed prior which is shared across the sequence rather than an auto-regressive prior as often used in prior work. We demonstrate our model’s success on three different video datasets (the popular benchmark CLEVRER; a simulated 3D Playroom environment; and a real-world Robotics Arm dataset). Finally, we analyze the contributions of the various model components and the representations learned by the model.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"16 1","pages":"10419-10427"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82547817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 34

Synchronization of Group-labelled Multi-graphs 群标记多图的同步

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.00639

Andrea Porfiri Dal Cin, L. Magri, F. Arrigoni, Andrea Fusiello, G. Boracchi

{"title":"Synchronization of Group-labelled Multi-graphs","authors":"Andrea Porfiri Dal Cin, L. Magri, F. Arrigoni, Andrea Fusiello, G. Boracchi","doi":"10.1109/ICCV48922.2021.00639","DOIUrl":"https://doi.org/10.1109/ICCV48922.2021.00639","url":null,"abstract":"Synchronization refers to the problem of inferring the unknown values attached to vertices of a graph where edges are labelled with the ratio of the incident vertices, and labels belong to a group. This paper addresses the synchronization problem on multi-graphs, that are graphs with more than one edge connecting the same pair of nodes. The problem naturally arises when multiple measures are available to model the relationship between two vertices. This happens when different sensors measure the same quantity, or when the original graph is partitioned into sub-graphs that are solved independently. In this case, the relationships among sub-graphs give rise to multi-edges and the problem can be traced back to a multi-graph synchronization. The baseline solution reduces multi-graphs to simple ones by averaging their multi-edges, however this approach falls short because: i) averaging is well defined only for some groups and ii) the resulting estimator is less precise and accurate, as we prove empirically. Specifically, we present MULTISYNC, a synchronization algorithm for multi-graphs that is based on a principled constrained eigenvalue optimization. MULTISYNC is a general solution that can cope with any linear group and we show to be profitably usable both on synthetic and real problems.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"34 1","pages":"6433-6443"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81289292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Persistent Homology based Graph Convolution Network for Fine-grained 3D Shape Segmentation 基于持久同调的图卷积网络细粒度三维形状分割

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.00701

Chi-Chong Wong, C. Vong

{"title":"Persistent Homology based Graph Convolution Network for Fine-grained 3D Shape Segmentation","authors":"Chi-Chong Wong, C. Vong","doi":"10.1109/ICCV48922.2021.00701","DOIUrl":"https://doi.org/10.1109/ICCV48922.2021.00701","url":null,"abstract":"Fine-grained 3D segmentation is an important task in 3D object understanding, especially in applications such as intelligent manufacturing or parts analysis for 3D objects. However, many challenges involved in such problem are yet to be solved, such as i) interpreting the complex structures located in different regions for 3D objects; ii) capturing fine-grained structures with sufficient topology correctness. Current deep learning and graph machine learning methods fail to tackle such challenges and thus provide inferior performance in fine-grained 3D analysis. In this work, methods in topological data analysis are incorporated with geometric deep learning model for the task of fine-grained segmentation for 3D objects. We propose a novel neural network model called Persistent Homology based Graph Convolution Network (PHGCN), which i) integrates persistent homology into graph convolution network to capture multi-scale structural information that can accurately represent complex structures for 3D objects; ii) applies a novel Persistence Diagram Loss (ℒPD) that provides sufficient topology correctness for segmentation over the fine-grained structures. Extensive experiments on fine-grained 3D segmentation validate the effectiveness of the proposed PHGCN model and show significant improvements over current state-of-the-art methods.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"1 1","pages":"7078-7087"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84367691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Efficient Action Recognition via Dynamic Knowledge Propagation 基于动态知识传播的高效动作识别

2021 IEEE/CVF International Conference on Computer Vision (ICCV) Pub Date : 2021-10-01 DOI: 10.1109/ICCV48922.2021.01346

Hanul Kim, Mihir Jain, Jun-Tae Lee, Sungrack Yun, F. Porikli

{"title":"Efficient Action Recognition via Dynamic Knowledge Propagation","authors":"Hanul Kim, Mihir Jain, Jun-Tae Lee, Sungrack Yun, F. Porikli","doi":"10.1109/ICCV48922.2021.01346","DOIUrl":"https://doi.org/10.1109/ICCV48922.2021.01346","url":null,"abstract":"Efficient action recognition has become crucial to extend the success of action recognition to many real-world applications. Contrary to most existing methods, which mainly focus on selecting salient frames to reduce the computation cost, we focus more on making the most of the selected frames. To this end, we employ two networks of different capabilities that operate in tandem to efficiently recognize actions. Given a video, the lighter network processes more frames while the heavier one only processes a few. In order to enable the effective interaction between the two, we propose dynamic knowledge propagation based on a cross-attention mechanism. This is the main component of our framework that is essentially a student-teacher architecture, but as the teacher model continues to interact with the student model during inference, we call it a dynamic student-teacher framework. Through extensive experiments, we demonstrate the effectiveness of each component of our framework. Our method outperforms competing state-of-the-art methods on two video datasets: ActivityNet-v1.3 and Mini-Kinetics.","PeriodicalId":6820,"journal":{"name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","volume":"42 2 1","pages":"13699-13708"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82859374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15