Mask-guided contrastive attention and two-stream metric co-learning for person Re-identification | |
Song, Chunfeng1,2![]() ![]() ![]() | |
发表期刊 | NEUROCOMPUTING
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ISSN | 0925-2312 |
2021-11-20 | |
卷号 | 465页码:561-573 |
产权排序 | 1 |
摘要 | Person Re-identification (ReID) is an important yet challenging task in computer vision. Due to diverse background clutters, variations of viewpoints and body poses, it is far from being solved. How to extract discriminative and robust features invariant to background clutters is one of the core problems. In this paper, we first introduce a set of binary segmentation masks to construct synthetic RGB-Mask pairs as inputs, and then design a mask-guided contrastive attention model (MGCAM) to learn features separately from the body and background regions. Moreover, we propose a novel region-level triplet loss to guide the features learning, i.e., pulling the features from the full image and body region close, whereas pushing the features from backgrounds away. To learn the similarities from multiple features of the proposed MGCAM, we further introduce the instance-level two-stream metric co-learning (TSMCL) to help learn pair-wise relations between features from not only different regions but also different instances. TSMCL could help learn more compact features across full and body streams, enhancing the performance of MGCAM. We evaluate the proposed method on four public datasets, including MARS, Market-1501, CUHK03, and DukeMTMC-reID. Extensive experiments show that the proposed method is effective and achieves satisfying results. (c) 2021 Elsevier B.V. All rights reserved. |
关键词 | Person ReID Contrastive attention model Two-stream metric learning |
DOI | 10.1016/j.neucom.2021.09.038 |
关键词[WOS] | NETWORK |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[62006231] ; National Natural Science Foundation of China[61525306] ; National Natural Science Foundation of China[61633021] ; National Natural Science Foundation of China[61721004] ; National Natural Science Foundation of China[61420106015] ; National Key Research and Development Program of China[2016YFB1001000] ; Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project)[2019JZZY010-119] ; Capital Science and Technology Leading Talent Training Project[Z181100006318030] ; NVIDIA ; NVIDIA DGX-1 AI Supercomputer |
项目资助者 | National Natural Science Foundation of China ; National Key Research and Development Program of China ; Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) ; Capital Science and Technology Leading Talent Training Project ; NVIDIA ; NVIDIA DGX-1 AI Supercomputer |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000702819100009 |
出版者 | ELSEVIER |
是否为代表性论文 | 否 |
七大方向——子方向分类 | 生物特征识别 |
国重实验室规划方向分类 | 多模态协同认知 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/45736 |
专题 | 模式识别实验室 |
通讯作者 | Shan, Caifeng |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences (CASIA) 2.Univ Chinese Acad Sci UCAS, Beijing, Peoples R China 3.Shandong Univ Sci & Technol, Qingdao, Peoples R China 4.CAS CAS AIR, Artificial Intelligence Res, Qingdao, Peoples R China |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Song, Chunfeng,Shan, Caifeng,Huang, Yan,et al. Mask-guided contrastive attention and two-stream metric co-learning for person Re-identification[J]. NEUROCOMPUTING,2021,465:561-573. |
APA | Song, Chunfeng,Shan, Caifeng,Huang, Yan,&Wang, Liang.(2021).Mask-guided contrastive attention and two-stream metric co-learning for person Re-identification.NEUROCOMPUTING,465,561-573. |
MLA | Song, Chunfeng,et al."Mask-guided contrastive attention and two-stream metric co-learning for person Re-identification".NEUROCOMPUTING 465(2021):561-573. |
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