PRDP: Person Reidentification With Dirty and Poor Data
文献类型：期刊论文
| 作者 | Xu, Furong4; Ma, Bingpeng4; Chang, Hong3,4; Shan, Shiguang1,2,4 |
| 刊名 | IEEE TRANSACTIONS ON CYBERNETICS
 |
| 出版日期 | 2021-09-01 |
| 页码 | 13 |
| 关键词 | Training Noise measurement Data models Task analysis Training data Predictive models Heuristic algorithms Dirty metric learning person reidentification (ReID) poor |
| ISSN号 | 2168-2267 |
| DOI | 10.1109/TCYB.2021.3105970 |
| 英文摘要 | In this article, we propose a novel method to simultaneously solve the data problem of dirty quality and poor quantity for person reidentification (ReID). Dirty quality refers to the wrong labels in image annotations. Poor quantity means that some identities have very few images (FewIDs). Training with these mislabeled data or FewIDs with triplet loss will lead to low generalization performance. To solve the label error problem, we propose a weighted label correction based on cross-entropy (wLCCE) strategy. Specifically, according to the influence range of the wrong labels, we first classify the mislabeled images into point label error and set label error. Then, we propose a weighted triplet loss (WTL) to correct the two label errors, respectively. To alleviate the poor quantity issue, we propose a feature simulation based on autoencoder (FSAE) method to generate some virtual samples for FewID. For the authenticity of the simulated features, we transfer the difference pattern of identities with multiple images (MultIDs) to FewIDs by training an autoencoder (AE)-based simulator. In this way, the FewIDs obtain richer expressions to distinguish from other identities. By dealing with a dirty and poor data problem, we can learn more robust ReID models using the triplet loss. We conduct extensive experiments on two public person ReID datasets: 1) Market-1501 and 2) DukeMTMC-reID, to verify the effectiveness of our approach. |
| 资助项目 | National Key Research and Development Program of China[2017YFA0700800] ; Natural Science Foundation of China (NSFC)[61876171] ; Natural Science Foundation of China (NSFC)[61976203] ; Open Project Fund from the Shenzhen Institute of Artificial Intelligence and Robotics for Society[AC01202005015] |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000732360900001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/18009]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Ma, Bingpeng |
| 作者单位 | 1.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Shanghai 200031, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 4.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xu, Furong,Ma, Bingpeng,Chang, Hong,et al. PRDP: Person Reidentification With Dirty and Poor Data[J]. IEEE TRANSACTIONS ON CYBERNETICS,2021:13. |
| APA | Xu, Furong,Ma, Bingpeng,Chang, Hong,&Shan, Shiguang.(2021).PRDP: Person Reidentification With Dirty and Poor Data.IEEE TRANSACTIONS ON CYBERNETICS,13. |
| MLA | Xu, Furong,et al."PRDP: Person Reidentification With Dirty and Poor Data".IEEE TRANSACTIONS ON CYBERNETICS (2021):13. |
入库方式： OAI收割
来源：计算技术研究所
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