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Pseudo Label Rectification With Joint Camera Shift Adaptation and Outlier Progressive Recycling for Unsupervised Person Re-Identification | |
Xu, Mingyuan1; Guo, Haiyun2,3; Jia, Yuheng4,5; Dai, Zhitao1; Wang, Jinqiao3,6 | |
发表期刊 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS |
ISSN | 1524-9050 |
2022-11-30 | |
页码 | 12 |
通讯作者 | Guo, Haiyun(haiyun.guo@nlpr.ia.ac.cn) ; Jia, Yuheng(yhjia@seu.edu.cn) |
摘要 | Person re-identification (re-ID) has many applications in intelligent transportation systems. Clustering-based methods, which alternate between the generation of pseudo labels via clustering and the optimization of the feature extractor, have obtained leading performance in unsupervised person re-ID. But there are still two issues not well addressed: 1) Most methods measure the feature similarity without considering the domain shift between cameras, degrading the clustering performance. 2) Outliers, which usually correspond to hard samples with large discrepancy from other images of the identical person, are in most cases directly excluded from the network training. To tackle the above issues, this paper proposes a plug-and-play pseudo label rectification framework, which jointly utilizes CAmera Shift adapTation module and Outlier progressive Recycling strategy ( $CASTOR$ ) to improve the quality of pseudo labels from both pre-clustering and post-clustering. Specifically, we first compute the camera similarity of two samples by utilizing a pretrained camera classification network and subtract the feature similarity by the camera similarity, the value of which is weighted in an exponential decay manner throughout the network training, in order to adaptively remedy the adverse impact of inter-camera distribution shift upon clustering. Besides, we carefully design an outlier progressive recycling strategy to reassign part of the outliers into the clustered groups to make full use of the useful information of outliers. Extensive experiments on three large scale unsupervised and unsupervised domain adaptive (UDA) person re-ID benchmarks validate the effectiveness of CASTOR and its wide compatibility with the state-of-the-art clustering-based methods. |
关键词 | Person re-identification unsupervised learning domain adaptation pseudo label rectification |
DOI | 10.1109/TITS.2022.3224233 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Program of China[2021ZD0110403] ; National Natural Science Foundation of China[62002356] ; National Natural Science Foundation of China[62106044] ; National Natural Science Foundation of China[62002357] ; National Natural Science Foundation of China[62076235] ; Natural Science Foundation of Jiangsu Province[BK20210221] ; Zhejiang Laboratory[2021KH0AB07] |
项目资助者 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu Province ; Zhejiang Laboratory |
WOS研究方向 | Engineering ; Transportation |
WOS类目 | Engineering, Civil ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
WOS记录号 | WOS:000912859500001 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51067 |
专题 | 紫东太初大模型研究中心_图像与视频分析 |
通讯作者 | Guo, Haiyun; Jia, Yuheng |
作者单位 | 1.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100864, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China 4.Southeast Univ, Key Lab Comp Network & Informat Integrat, Minist Educ, Nanjing 210096, Peoples R China 5.Southeast Univ, Sch Comp Sci & Engn, Nanjing 210096, Peoples R China 6.Peng Cheng Lab, Shenzhen 518066, Peoples R China |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Xu, Mingyuan,Guo, Haiyun,Jia, Yuheng,et al. Pseudo Label Rectification With Joint Camera Shift Adaptation and Outlier Progressive Recycling for Unsupervised Person Re-Identification[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2022:12. |
APA | Xu, Mingyuan,Guo, Haiyun,Jia, Yuheng,Dai, Zhitao,&Wang, Jinqiao.(2022).Pseudo Label Rectification With Joint Camera Shift Adaptation and Outlier Progressive Recycling for Unsupervised Person Re-Identification.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,12. |
MLA | Xu, Mingyuan,et al."Pseudo Label Rectification With Joint Camera Shift Adaptation and Outlier Progressive Recycling for Unsupervised Person Re-Identification".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2022):12. |
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