Opto-Electronic Engineering, Volume. 52, Issue 1, 240238(2025)

Multi-scale feature interaction pseudo-label unsupervised domain adaptation for person re-identification

Zhongmin Liu1,*... Fujun Yang1 and Wenjin Hu2 |Show fewer author(s)
Author Affiliations
  • 1Department of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou, Gansu 730050, China
  • 2College of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, Gansu 730030, China
  • show less
    References(43)

    [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).

    [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).

    [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).

    [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).

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

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

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

    [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).

    [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).

    [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).

    Tools

    Get Citation

    Copy Citation Text

    Zhongmin Liu, Fujun Yang, Wenjin Hu. Multi-scale feature interaction pseudo-label unsupervised domain adaptation for person re-identification[J]. Opto-Electronic Engineering, 2025, 52(1): 240238

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    Category: Article

    Received: Oct. 11, 2024

    Accepted: Dec. 18, 2024

    Published Online: Feb. 21, 2025

    The Author Email: Liu Zhongmin (刘仲民)

    DOI:10.12086/oee.2025.240238

    Topics