Domain Adaptive Attention Learning for Unsupervised Person Re-Identification
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2019
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| _version_ | 1866909224169111552 |
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| author | Huang, Yangru Peng, Peixi Jin, Yi Li, Yidong Xing, Junliang Ge, Shiming |
| author_facet | Huang, Yangru Peng, Peixi Jin, Yi Li, Yidong Xing, Junliang Ge, Shiming |
| contents | Person re-identification (Re-ID) across multiple datasets is a challenging task due to two main reasons: the presence of large cross-dataset distinctions and the absence of annotated target instances. To address these two issues, this paper proposes a domain adaptive attention learning approach to reliably transfer discriminative representation from the labeled source domain to the unlabeled target domain. In this approach, a domain adaptive attention model is learned to separate the feature map into domain-shared part and domain-specific part. In this manner, the domain-shared part is used to capture transferable cues that can compensate cross-dataset distinctions and give positive contributions to the target task, while the domain-specific part aims to model the noisy information to avoid the negative transfer caused by domain diversity. A soft label loss is further employed to take full use of unlabeled target data by estimating pseudo labels. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 benchmarks demonstrate the proposed approach outperforms the state-of-the-arts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1905_10529 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Domain Adaptive Attention Learning for Unsupervised Person Re-Identification Huang, Yangru Peng, Peixi Jin, Yi Li, Yidong Xing, Junliang Ge, Shiming Computer Vision and Pattern Recognition Person re-identification (Re-ID) across multiple datasets is a challenging task due to two main reasons: the presence of large cross-dataset distinctions and the absence of annotated target instances. To address these two issues, this paper proposes a domain adaptive attention learning approach to reliably transfer discriminative representation from the labeled source domain to the unlabeled target domain. In this approach, a domain adaptive attention model is learned to separate the feature map into domain-shared part and domain-specific part. In this manner, the domain-shared part is used to capture transferable cues that can compensate cross-dataset distinctions and give positive contributions to the target task, while the domain-specific part aims to model the noisy information to avoid the negative transfer caused by domain diversity. A soft label loss is further employed to take full use of unlabeled target data by estimating pseudo labels. Extensive experiments on the Market-1501, DukeMTMC-reID and MSMT17 benchmarks demonstrate the proposed approach outperforms the state-of-the-arts. |
| title | Domain Adaptive Attention Learning for Unsupervised Person Re-Identification |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1905.10529 |