Learning to Adapt Across Dual Discrepancy for Cross-Domain Person Re-Identification
文献类型：期刊论文
| 作者 | Luo, Chuanchen2,3 ; Song, Chunfeng2,3; Zhang, Zhaoxiang1,2,3,4
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| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| 出版日期 | 2023-02-01 |
| 卷号 | 45期号:2页码:1963-1980 |
| 关键词 | Person re-identification domain adaptation cross-domain mixup camera-aware learning self-paced learning |
| ISSN号 | 0162-8828 |
| DOI | 10.1109/TPAMI.2022.3167053 |
| 通讯作者 | Zhang, Zhaoxiang(zhaoxiang.zhang@ia.ac.cn) |
| 英文摘要 | Thanks to the advent of deep neural networks, recent years have witnessed rapid progress in person re-identification (re-ID). Deep-learning-based methods dominate the leadership of large-scale benchmarks, some of which even surpass the human-level performance. Despite their impressive performance under the single-domain setup, current fully-supervised re-ID models degrade significantly when transplanted to an unseen domain. According to the characteristics of the re-ID task, such degradation is mainly attributed to the dramatic variation within the target domain and the severe shift between the source and target domain, which we call dual discrepancy in this paper. To achieve a model that generalizes well to the target domain, it is desirable to take such dual discrepancy into account. In terms of the former issue, a prevailing solution is to enforce consistency between nearest-neighbors in the embedding space. However, we find that the search of neighbors is highly biased in our case due to the discrepancy across cameras. For this reason, we equip the vanilla neighborhood invariance approach with a camera-aware learning scheme. As for the latter issue, we propose a novel cross-domain mixup scheme. It works in conjunction with virtual prototypes which are employed to handle the disjoint label space between the two domains. In this way, we can realize the smooth transfer by introducing the interpolation between the two domains as a transition state. Extensive experiments on four public benchmarks demonstrate the superiority of our method. Without any auxiliary models and offline clustering procedure, it achieves competitive performance against existing state-of-the-art methods. The code is available at https://github.com/LuckyDC/generalizing-reid-improved. |
| 资助项目 | Major Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of China[61836014] ; National Natural Science Foundation of China[U21B2042] ; National Natural Science Foundation of China[62072457] ; National Natural Science Foundation of China[62006231] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000912386000041 |
| 出版者 | IEEE COMPUTER SOC |
| 资助机构 | Major Project for New Generation of AI ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/51341]  |
| 专题 | 多模态人工智能系统全国重点实验室 自动化研究所_智能感知与计算研究中心 |
| 通讯作者 | Zhang, Zhaoxiang |
| 作者单位 | 1.HKISI CAS, Ctr Artificial Intelligence & Robot, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100045, Peoples R China 4.Chinese Acad Sci, Beijing 100045, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, Chuanchen,Song, Chunfeng,Zhang, Zhaoxiang. Learning to Adapt Across Dual Discrepancy for Cross-Domain Person Re-Identification[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2023,45(2):1963-1980. |
| APA | Luo, Chuanchen,Song, Chunfeng,&Zhang, Zhaoxiang.(2023).Learning to Adapt Across Dual Discrepancy for Cross-Domain Person Re-Identification.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,45(2),1963-1980. |
| MLA | Luo, Chuanchen,et al."Learning to Adapt Across Dual Discrepancy for Cross-Domain Person Re-Identification".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 45.2(2023):1963-1980. |
入库方式： OAI收割
来源：自动化研究所
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