[1] X Y Zhang, B H Zhang, X Q Lv et al. The joint discriminative and generative learning for person re-identification of deep dual attention. Opto-Electron Eng, 48, 200388(2021).

[2] Z Zhong, L Zheng, Z M Luo et al. Invariance matters: exemplar memory for domain adaptive person re-identification, 598-607(2019). https://doi.org/10.1109/CVPR.2019.00069

[3] Y X Ge, F Zhu, D P Chen et al. Self-paced contrastive learning with hybrid memory for domain adaptive object re-ID, 949(2020). https://doi.org/10.5555/3495724.3496673

[4] Z Z Dai, G Y Wang, W H Yuan et al. Cluster contrast for unsupervised person re-identification, 319-337(2023). https://doi.org/10.1007/978-3-031-26351-4_20

[5] J J Tian, Q H Tang, R Li et al. A camera identity-guided distribution consistency method for unsupervised multi-target domain person re-identification. ACM Trans Intell Syst Technol, 12, 38(2021).

[6] Y Choi, M Choi, M Kim et al. StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation, 8789-8797(2018). https://doi.org/10.1109/CVPR.2018.00916

[7] F X Yang, Z Zhong, Z M Luo et al. Joint noise-tolerant learning and meta camera shift adaptation for unsupervised person re-identification, 4853-4862(2021). https://doi.org/10.1109/CVPR46437.2021.00482

[8] S Y Xuan, S L Zhang. Intra-inter camera similarity for unsupervised person re-identification, 11921-11930(2021). https://doi.org/10.1109/CVPR46437.2021.01175

[9] M L Wang, B S Lai, J Q Huang et al. Camera-aware proxies for unsupervised person re-identification, 2764-2772(2021). https://doi.org/10.1609/aaai.v35i4.16381

[10] X Li, T F Liang, Y Jin et al. Camera-aware style separation and contrastive learning for unsupervised person re-identification, 1-6(2022). https://doi.org/10.1109/ICME52920.2022.9859842

[11] G Lee, S Lee, D Kim et al. Camera-driven representation learning for unsupervised domain adaptive person re-identification(2023). https://doi.org/10.48550/arXiv.2308.11901

[12] Y X Ge, D P Chen, H S Li. Mutual mean-teaching: pseudo label refinery for unsupervised domain adaptation on person re-identification(2020).

[13] Y P Zhai, Q X Ye, S J Lu et al. Multiple expert brainstorming for domain adaptive person re-identification, 594-611(2020). https://doi.org/10.1007/978-3-030-58571-6_35

[14] X Zhang, Y X Ge, Y Qiao et al. Refining pseudo labels with clustering consensus over generations for unsupervised object re-identification, 3435-3444(2021). https://doi.org/10.1109/CVPR46437.2021.00344

[15] Y Cho, W J Kim, S Hong et al. Part-based pseudo label refinement for unsupervised person re-identification, 7298-7308(2022). https://doi.org/10.1109/CVPR52688.2022.00716

[16] Y H Wu, T T Huang, H T Yao et al. Multi-centroid representation network for domain adaptive person re-ID, 2750-2758(2022). https://doi.org/10.1609/aaai.v36i3.20178

[17] H Chen, B Lagadec, F Bremond. ICE: inter-instance contrastive encoding for unsupervised person re-identification, 14940-14949(2021). https://doi.org/10.1109/ICCV48922.2021.01469

[18] L Lan, X Teng, J Zhang et al. Learning to purification for unsupervised person re-identification. IEEE Trans Image Process, 33, 3338-3353(2023).

[19] Z Q Chen, Z C Cui, C Zhang et al. Dual clustering co-teaching with consistent sample mining for unsupervised person re-identification. IEEE Trans Circuits Syst Video Technol, 33, 5908-5920(2023).

[20] Z Q Pang, L L Zhao, Q Y Liu et al. Camera invariant feature learning for unsupervised person re-identification. IEEE Trans Multimed, 25, 6171-6182(2023).

[21] H J Wang, M Yang, J L Liu et al. Pseudo-label noise prevention, suppression and softening for unsupervised person re-identification. IEEE Trans Inf Forensics Secur, 18, 3222-3237(2023).

[22] P N Li, K Y Wu, S P Zhou et al. Pseudo labels refinement with intra-camera similarity for unsupervised person re-identification, 366-370(2023). https://doi.org/10.1109/ICIP49359.2023.10222317

[23] L Zheng, L Y Shen, L Tian et al. Scalable person re-identification: a benchmark, 1116-1124(2015). https://doi.org/10.1109/ICCV.2015.133

[24] L H Wei, S L Zhang, W Gao et al. Person transfer GAN to bridge domain gap for person re-identification, 79-88(2018). https://doi.org/10.1109/CVPR.2018.00016

[25] X X Sun, L Zheng. Dissecting person re-identification from the viewpoint of viewpoint, 608-617(2019). https://doi.org/10.1109/CVPR.2019.00070

[26] M Ester, H P Kriegel, J Sander et al. A density-based algorithm for discovering clusters in large spatial databases with noise, 226-231(1996). https://doi.org/10.5555/3001460.3001507

[27] D Y Zhou, O Bousquet, T N Lal et al. Learning with local and global consistency, 321-328(2003). https://doi.org/10.5555/2981345.2981386

[28] J Deng, W Dong, R Socher et al. ImageNet: a large-scale hierarchical image database, 248-255(2009). https://doi.org/10.1109/CVPR.2009.5206848

[29] K M He, X Y Zhang, S Q Ren et al. Deep residual learning for image recognition, 770-778(2016). https://doi.org/10.1109/CVPR.2016.90

[30] Z Zhong, L Zheng, D L Cao et al. Re-ranking person re-identification with k-reciprocal encoding, 3652-3661(2017). https://doi.org/10.1109/CVPR.2017.389

[31] K C Zheng, W Liu, L X He et al. Group-aware label transfer for domain adaptive person re-identification, 5306-5315(2021). https://doi.org/10.1109/CVPR46437.2021.00527

[32] H Li, X W Zhang, X P Zhao et al. Multi-label cooperative learning for cross domain person re-identification. J Beijing Univ Aeronaut Astronaut, 48, 1534-1542(2022).

[33] Y X Liu, H W Ge, L Sun et al. Complementary attention-driven contrastive learning with hard-sample exploring for unsupervised domain adaptive person re-ID. IEEE Trans Circuits Syst Video Technol, 33, 326-341(2023).

[34] L W Chen, F Ye, T Q Huang et al. An unsupervised person re-Identification method based on intra-/inter-camera merger. J Comput Res Dev, 60, 415-425(2023).

[35] H W Zhang, G Q Zhang, Y H Chen et al. Global relation-aware contrast learning for unsupervised person re-identification. IEEE Trans Circuits Syst Video Technol, 32, 8599-8610(2022).

[36] Y P Qian, F S Wang, L Xiong. Unsupervised person re-identification method based on local refinement multi-branch and global feature sharing. J Electron Meas Instrum, 37, 106-115(2023).

[37] J J Peng, G Q Jiang, H B Wang. Adaptive memorization with group labels for unsupervised person re-identification. IEEE Trans Circuits Syst Video Technol, 33, 5802-5813(2023).

[38] F Wang, H P Liu. Understanding the behaviour of contrastive loss, 2495-2504(2021). https://doi.org/10.1109/CVPR46437.2021.00252