Multi-source domain generalization peron re-identification with knowledge accumulation and distribution enhancement
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-024-05266-8.pdf
Reference47 articles.
1. Wang J, Lan C, Liu C, Ouyang Y, Qin T, Lu W et al (2022) Generalizing to unseen domains: a survey on domain generalization. Knowl Data Eng Trans. https://doi.org/10.1109/TKDE.2022.3178128
2. Song J, Yang Y, Song Y, Xiang T et al (2019) Generalizable person re-identification by domain-invariant mapping network. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). CVPR, pp 2288–2295
3. Zhao Y, Zhong Z, Yang F, Luo Z, Lin Y, Li S et al (2021) Learning to generalize unseen domains via memory-based multi-source meta learning for person re-identification. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR46437.2021.00621
4. Jin X, Lan C, Zeng W, Chen Z, Zhang L (2020) Style normalization and restitution for generalizable person re-identification. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR42600.2020.00321
5. Dai Y, Li X, Liu J, Tong Z, Duan L (2021) Generalizable person reidentification with relevance-aware mixture of experts. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR46437.2021.01588
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