The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-Identification
Open accessAbstract
This paper considers the domain adaptive person re-identification (re-ID) problem: learning a re-ID model from a labeled source domain and an unlabeled target domain. Conventional methods are mainly to reduce feature distribution gap between the source and target domains. However, these studies largely neglect the intra-domain variations in the target domain, which contain critical factors influencing the testing performance on the target domain. In this work, we comprehensively investigate into the intra-domain variations of the target domain and propose to generalize the re-ID model w.r.t three types of the underlying invariance, i.e., exemplar-invariance, camera-invariance and neighborhood-invariance. To achieve this goal, an exemplar memory is introduced to store features of the target domain and accommodate the three invariance properties. The memory allows us to enforce the invariance constraints over global training batch without significantly increasing computation cost. Experiment demonstrates that the three invariance properties and the proposed memory are indispensable towards an effective domain adaptation system. Results on three re-ID domains show that our domain adaptation accuracy outperforms the state of the art by a large margin. Code is available at: https://github.com/zhunzhong07/ECN.
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Wilson, G., & Cook, D. J. (2020) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3400066 · Cited by 987
Style Normalization and Restitution for Generalizable Person Re-Identification
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Unsupervised Person Re-Identification via Multi-Label Classification
Wang, D., & Zhang, S. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01099 · Cited by 373
ICE: Inter-instance Contrastive Encoding for Unsupervised Person Re-identification
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Unsupervised person re-identification with multi-label learning guided self-paced clustering
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Metadata
- Authors
- Zhong, Z., Zheng, L., Luo, Z., Li, S., & Yang, Y.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 598-607
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2962859295
- Cited by
- 685
- References
- 41
- FWCI
- 36.94
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhong, Z., Zheng, L., Luo, Z., Li, S., & Yang, Y. (2019). Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-Identification. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00069
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