[1] 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

[2] W J Deng, L Zheng, Q X Ye et al. Image-image domain adaptation with preserved self-similarity and domain-dissimilarity for person re-identification, 994-1003(2018). https://doi.org/10.1109/CVPR.2018.00110

[3] Y Fu, Y C Wei, G S Wang et al. Self-similarity grouping: a simple unsupervised cross domain adaptation approach for person re-identification, 6112-6121(2019). https://doi.org/10.1109/ICCV.2019.00621

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

[5] T He, L Q Shen, Y C Guo et al. SECRET: self-consistent pseudo label refinement for unsupervised domain adaptive person re-identification, 879-887(2022). https://doi.org/10.1609/aaai.v36i1.19970

[6] M Ye, A J Ma, L Zheng et al. Dynamic label graph matching for unsupervised video re-identification, 5142-5150(2017). https://doi.org/10.1109/ICCV.2017.550

[7] H H Fan, L Zheng, C G Yan et al. Unsupervised person re-identification: clustering and fine-tuning. ACM Trans Multimedia Comput Commun Appl, 14, 83(2018).

[8] H X Yu, W S Zheng, A C Wu et al. Unsupervised person re-identification by soft multilabel learning, 2148-2157(2019). https://doi.org/10.1109/CVPR.2019.00225

[9] Q Z Yang, H X Yu, A C Wu et al. Patch-based discriminative feature learning for unsupervised person re-identification, 3633-3642(2019). https://doi.org/10.1109/CVPR.2019.00375

[10] S Y Xuan, S L Zhang. Intra-inter camera similarity for unsupervised person re-identification, 11926-11935(2021). https://doi.org/10.1109/CVPR46437.2021.01175

[11] X M Han, X H Yu, G R Li et al. Rethinking sampling strategies for unsupervised person re-identification. IEEE Trans Image Process, 32, 29-42(2023).

[12] S N Chen, Z Y Fan, J Y Yin. Pseudo label based on multiple clustering for unsupervised cross-domain person re-identification. IEEE Signal Process Lett, 27, 1460-1464(2020).

[13] J N Li, S L Zhang. Joint visual and temporal consistency for unsupervised domain adaptive person re-identification, 483-499(2020). https://doi.org/10.1007/978-3-030-58586-0_29

[14] S M Yu, S J Wang. Consistency mean-teaching for unsupervised domain adaptive person re-identification, 159-166(2022). https://doi.org/10.1145/3512388.3512451

[15] 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).

[16] H Song, M Kim, D Park et al. Learning from noisy labels with deep neural networks: a survey. IEEE Trans Neural Netw Learn Syst, 34, 8135-8153(2023).

[17] 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).

[18] M Jaderberg, K Simonyan, A Zisserman et al. Spatial transformer networks, 2017-2025(2015).

[19] D Hendrycks, K Gimpel. Gaussian error linear units (GELUs)(2016). https://arxiv.org/abs/1606.08415

[20] A El-Nouby, H Touvron, M Caron et al. XCiT: cross-covariance image transformers, 1531(2021).

[21] J L Ba, J R Kiros, G E Hinton. Layer normalization(2016). https://arxiv.org/abs/1607.06450

[22] A Q Mao, M Mohri, Y T Zhong. Cross-entropy loss functions: theoretical analysis and applications(2023).

[23] F Schroff, D Kalenichenko, J Philbin. FaceNet: a unified embedding for face recognition and clustering, 815-823(2015). https://doi.org/10.1109/CVPR.2015.7298682

[24] 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

[25] E Ristani, F Solera, R Zou et al. Performance measures and a data set for multi-target, multi-camera tracking, 17-35(2016). https://doi.org/10.1007/978-3-319-48881-3_2

[26] Z Zhong, L Zheng, G L Kang et al. Random erasing data augmentation, 13001-13008(2020). https://doi.org/10.1609/aaai.v34i07.7000

[27] J Hu, L Shen, G Sun. Squeeze-and-excitation networks, 7132-7141(2018). https://doi.org/10.1109/CVPR.2018.00745

[28] S Woo, J Park, J Y Lee et al. CBAM: convolutional block attention module, 3-19(2018). https://doi.org/10.1007/978-3-030-01234-2_1

[29] Q B Hou, D Q Zhou, J S Feng. Coordinate attention for efficient mobile network design, 13713-13722(2021). https://doi.org/10.1109/CVPR46437.2021.01350

[30] Z Zhong, L Zheng, S Z Li et al. Generalizing a person retrieval model hetero-and homogeneously, 172-188(2018). https://doi.org/10.1007/978-3-030-01261-8_11

[31] 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

[32] S A Zhang, H F Hu. Unsupervised person re-identification using unified domanial learning. Neural Process Lett, 55, 6887-6905(2023).

[33] Y J Li, C S Lin, Y B Lin et al. Cross-dataset person re-identification via unsupervised pose disentanglement and adaptation, 7919-7929(2019). https://doi.org/10.1109/ICCV.2019.00801

[34] X Y Zhang, J W Cao, C H Shen et al. Self-training with progressive augmentation for unsupervised cross-domain person re-identification, 8222-8231(2019). https://doi.org/10.1109/ICCV.2019.00831

[35] D K Wang, S L Zhang. Unsupervised person re-identification via multi-label classification, 10981-10990(2020). https://doi.org/10.1109/CVPR42600.2020.01099

[36] X Jin, C L Lan, W J Zeng et al. Style normalization and restitution for generalizable person re-identification, 3143-3152(2020). https://doi.org/10.1109/CVPR42600.2020.00321

[37] Z Zhong, L Zheng, Z M Luo et al. Learning to adapt invariance in memory for person re-identification. IEEE Trans Pattern Anal Mach Intell, 43, 2723-2738(2021).

[38] Y P Zhai, S J Lu, Q X Ye et al. AD-cluster: augmented discriminative clustering for domain adaptive person re-identification, 9021-9030(2020). https://doi.org/10.1109/CVPR42600.2020.00904

[39] 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

[40] H Chen, Y H Wang, B Lagadec et al. Learning invariance from generated variance for unsupervised person re-identification. IEEE Trans Pattern Anal Mach Intell, 45, 7494-7508(2023).

[41] K C Zheng, C L Lan, W J Zeng et al. Exploiting sample uncertainty for domain adaptive person re-identification, 3538-3546(2021). https://doi.org/10.1609/aaai.v35i4.16468

[42] Y F Li, X D Zhu, J Sun et al. Unsupervised person re-identification based on high-quality pseudo labels. Appl Intell, 53, 15112-15126(2023).

[43] Y Zhao, Q Y Shu, X Shi et al. Unsupervised person re-identification by dynamic hybrid contrastive learning. Image Vis Comput, 137, 104786(2023).