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
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
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    References(43)

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

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

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

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

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

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

    [17] Ester M, Kriegel H P, Sander J et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C], 226-231(1996).

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

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

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

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

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

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

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

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

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    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

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    Paper Information

    Category: Article

    Received: Oct. 11, 2024

    Accepted: Dec. 18, 2024

    Published Online: Feb. 21, 2025

    The Author Email: Zhongmin Liu (刘仲民)

    DOI:10.12086/oee.2025.240238

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