Feature Refinement and Filter Network for Person Re-Identification
In the task of person re-identification, the attention mechanism and fine-grained information have been proved to be effective. However, it has been observed that models often focus on the extraction of features with strong discrimination, and neglect other valuable features. The extracted fine-grai...
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| Published in | IEEE transactions on circuits and systems for video technology Vol. 31; no. 9; pp. 3391 - 3402 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.09.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | In the task of person re-identification, the attention mechanism and fine-grained information have been proved to be effective. However, it has been observed that models often focus on the extraction of features with strong discrimination, and neglect other valuable features. The extracted fine-grained information may include redundancies. In addition, current methods lack an effective scheme to remove background interference. Therefore, this paper proposes the feature refinement and filter network to solve the above problems from three aspects: first, by weakening the high response features, we aim to identify highly valuable features and extract the complete features of persons, thereby enhancing the robustness of the model; second, by positioning and intercepting the high response areas of persons, we eliminate the interference arising from background information and strengthen the response of the model to the complete features of persons; finally, valuable fine-grained features are selected using a multi-branch attention network for person re-identification to enhance the performance of the model. Our extensive experiments on the benchmark Market-1501, DukeMTMC-reID, CUHK03 and MSMT17 person re-identification datasets demonstrate that the performance of our method is comparable to that of state-of-the-art approaches. |
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| AbstractList | In the task of person re-identification, the attention mechanism and fine-grained information have been proved to be effective. However, it has been observed that models often focus on the extraction of features with strong discrimination, and neglect other valuable features. The extracted fine-grained information may include redundancies. In addition, current methods lack an effective scheme to remove background interference. Therefore, this paper proposes the feature refinement and filter network to solve the above problems from three aspects: first, by weakening the high response features, we aim to identify highly valuable features and extract the complete features of persons, thereby enhancing the robustness of the model; second, by positioning and intercepting the high response areas of persons, we eliminate the interference arising from background information and strengthen the response of the model to the complete features of persons; finally, valuable fine-grained features are selected using a multi-branch attention network for person re-identification to enhance the performance of the model. Our extensive experiments on the benchmark Market-1501, DukeMTMC-reID, CUHK03 and MSMT17 person re-identification datasets demonstrate that the performance of our method is comparable to that of state-of-the-art approaches. |
| Author | Li, Weijun Gong, Ke Bai, Xiao Ning, Xin Zhang, Liping Tian, Shengwei |
| Author_xml | – sequence: 1 givenname: Xin orcidid: 0000-0001-7897-1673 surname: Ning fullname: Ning, Xin email: ningxin@semi.ac.cn organization: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China – sequence: 2 givenname: Ke orcidid: 0000-0002-4767-5916 surname: Gong fullname: Gong, Ke email: gongke@wavewisdom-bj.com organization: Cognitive Computing Technology Joint Laboratory, Wave Group, Beijing, China – sequence: 3 givenname: Weijun orcidid: 0000-0001-9668-2883 surname: Li fullname: Li, Weijun email: wjli@semi.ac.cn organization: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China – sequence: 4 givenname: Liping orcidid: 0000-0001-6508-3757 surname: Zhang fullname: Zhang, Liping email: zliping@semi.ac.cn organization: Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China – sequence: 5 givenname: Xiao surname: Bai fullname: Bai, Xiao email: baixiao@buaa.edu.cn organization: State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Jiangxi Research Institute, Beihang University, Beijing, China – sequence: 6 givenname: Shengwei orcidid: 0000-0003-3525-5102 surname: Tian fullname: Tian, Shengwei organization: School of Software, Xinjiang University, Xinjiang, China |
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| Snippet | In the task of person re-identification, the attention mechanism and fine-grained information have been proved to be effective. However, it has been observed... |
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| StartPage | 3391 |
| SubjectTerms | attention deep learning Feature extraction Image recognition Information filters Interference Person re-identification Person Search Robustness Task analysis Training |
| Title | Feature Refinement and Filter Network for Person Re-Identification |
| URI | https://ieeexplore.ieee.org/document/9285312 https://www.proquest.com/docview/2568777300 |
| Volume | 31 |
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