Learning Degradation-Invariant Representation for Robust Real-World Person Re-Identification
文献类型：期刊论文
| 作者 | Huang, Yukun2; Fu, Xueyang2; Li, Liang1; Zha, Zheng-Jun2 |
| 刊名 | INTERNATIONAL JOURNAL OF COMPUTER VISION
 |
| 出版日期 | 2022-09-01 |
| 页码 | 27 |
| 关键词 | Person Re-ID Representation learning Vision in bad weather Deep learning Low-light image enhancement |
| ISSN号 | 0920-5691 |
| DOI | 10.1007/s11263-022-01666-w |
| 英文摘要 | Person re-identification (Re-ID) in real-world scenarios suffers from various degradations, e.g., low resolution, weak lighting, and bad weather. These degradations hinders identity feature learning and significantly degrades Re-ID performance. To address these issues, in this paper, we propose a degradation invariance learning framework for robust person Re-ID. Concretely, we first design a content-degradation feature disentanglement strategy to capture and isolate task-irrelevant features contained in the degraded image. Then, to avoid the catastrophic forgetting problem, we introduce a memory replay algorithm to further consolidate invariance knowledge learned from the previous pre-training to improve subsequent identity feature learning. In this way, our framework is able to continuously maintain degradation-invariant priors from one or more datasets to improve the robustness of identity features, achieving state-of-the-art Re-ID performance on several challenging real-world benchmarks with a unified model. Furthermore, the proposed framework can be extended to low-level image processing, e.g., low-light image enhancement, demonstrating the potential of our method as a general framework for the various vision tasks. Code and trained models will be available at: https://github.com/hyk1996/Degradati on-Invariant-Re-D-pytorch. |
| 资助项目 | National Key R&D Program of China[2020AAA0105702] ; National Natural Science Foundation of China (NSFC)[U19B2038] ; National Natural Science Foundation of China (NSFC)[61901433] ; University Synergy Innovation Program of Anhui Province[GXXT-2019-025] ; Fundamental Research Funds for the Central Universities[WK2100000024] ; USTC Research Funds of the Double First-Class Initiative[YD2100002003] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000849289700001 |
| 出版者 | SPRINGER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19428]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Fu, Xueyang |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China 2.Univ Sci & Technol China, Hefei, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Yukun,Fu, Xueyang,Li, Liang,et al. Learning Degradation-Invariant Representation for Robust Real-World Person Re-Identification[J]. INTERNATIONAL JOURNAL OF COMPUTER VISION,2022:27. |
| APA | Huang, Yukun,Fu, Xueyang,Li, Liang,&Zha, Zheng-Jun.(2022).Learning Degradation-Invariant Representation for Robust Real-World Person Re-Identification.INTERNATIONAL JOURNAL OF COMPUTER VISION,27. |
| MLA | Huang, Yukun,et al."Learning Degradation-Invariant Representation for Robust Real-World Person Re-Identification".INTERNATIONAL JOURNAL OF COMPUTER VISION (2022):27. |
入库方式： OAI收割
来源：计算技术研究所
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