Infrared Technology, Volume. 47, Issue 5, 628(2025)

Soft-Weight Prototype Contrastive Learning for Unsupervised Visible-Infrared Person Re-Identification

Hanshuo ZHAO1, Yiwen MA2, Yanxia ZHANG1, Pei WANG3, and Jianwei YANG4、*
Author Affiliations
  • 1School of Mechanical and Electrical Engineering, Henan Open University, Zhengzhou 450046, China
  • 2School of Computer Science, Zhengzhou University of Aeronautics, Zhengzhou 450046, China
  • 3School of Architecture and Intelligent Construction, Henan Open University, Zhengzhou 450046, China
  • 4School of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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    References(38)

    [1] [1] GE W, PAN C, WU A, et al. Cross-camera feature prediction for intra-camera supervised person re-identification across distant scenes[C]//Proceedings of the29th ACM International Conference on Multimedia, 2021: 3644-3653.

    [2] [2] FU Y, WEI Y, WANG G, et al. Self-similarity grouping: A simple unsupervised cross domain adaptation approach for person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 6112-6121.

    [3] [3] GUO J, YUAN Y, HUANG L, et al. Beyond human parts: Dual part-aligned representations for person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 3642-3651.

    [4] [4] ZHANG Q, LAI C, LIU J, et al. Fmcnet: Feature-level modality compensation for visible-infrared person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 7349-7358.

    [6] [6] YANG B, CHEN J, YE M. Top-k visual tokens transformer: Selecting tokens for visible-infrared person re-identification[C]//ICASSP2023-2023IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP).IEEE, 2023: 1-5.

    [7] [7] ZHANG Y, WANG H. Diverse embedding expansion network and low-light cross-modality benchmark for visible-infrared person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 2153-2162.

    [8] [8] YANG M, HUANG Z, HU P, et al. Learning with twin noisy labels for visible-infrared person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 14308-14317.

    [9] [9] Park H, Lee S, Lee J, et al. Learning by aligning: Visible-infrared person re-identification using cross-modal correspondences[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 12046-12055.

    [10] [10] YE M, RUAN W, DU B, et al. Channel augmented joint learning for visible-infrared recognition[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 13567-13576.

    [11] [11] WANG G, ZHANG T, CHENG J, et al. RGB-infrared cross-modality person re-identification via joint pixel and feature alignment[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019: 3623-3632.

    [12] [12] YANG Y, ZHANG T, CHENG J, et al. Cross-modality paired-images generation and augmentation for RGB-infrared person re-identification[J].Neural Networks, 2020,128: 294-304.

    [13] [13] LI D, WEI X, HONG X, et al. Infrared-visible cross-modal person re-identification with an x modality[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2020,34(4): 4610-4617.

    [14] [14] ZHANG Y, YAN Y, LU Y, et al. Towards a unified middle modality learning for visible-infrared person re-identification[C]//Proceedings of the 29th ACM International Conference on Multimedia, 2021: 788-796.

    [15] [15] WEI Z, YANG X, WANG N, et al. Syncretic modality collaborative learning for visible infrared person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021: 225-234.

    [16] [16] LIU J, SUN Y, ZHU F, et al. Learning memory-augmented unidirectional metrics for cross-modality person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 19366-19375.

    [17] [17] YE M, SHEN J, J. Crandall D, et al. Dynamic dual-attentive aggregation learning for visible-infrared person re-identification[C]//Computer Vision–ECCV, 2020: 229-247.

    [18] [18] WU Q, DAI P, CHEN J, et al. Discover cross-modality nuances for visible-infrared person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 4330-4339.

    [19] [19] SUN H, LIU J, ZHANG Z, et al. Not all pixels are matched: Dense contrastive learning for cross-modality person re-identification[C]//Proceedings of the 30th ACM International Conference on Multimedia, 2022: 5333-5341.

    [20] [20] FANG X, YANG Y, FU Y. Visible-infrared person re-identification via semantic alignment and affinity inference[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023: 11270-11279.

    [21] [21] FENG J, WU A, ZHENG W S. Shape-erased feature learning for visible-infrared person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 22752-22761.

    [22] [22] LIANG W, WANG G, LAI J, et al. Homogeneous-to-heterogeneous: Unsupervised learning for RGB-infrared person re-identification[J].IEEE Transactions on Image Processing, 2021,30: 6392-6407.

    [23] [23] WANG J, ZHANG Z, CHEN M, et al. Optimal transport for label-efficient visible-infrared person re-identification[C]//European Conference on Computer Vision, 2022: 93-109.

    [24] [24] DAI Z, WANG G, YUAN W, et al. Cluster contrast for unsupervised person re-identification[C]//Proceedings of the Asian Conference on Computer Vision, 2022: 1142-1160.

    [25] [25] YANG B, YE M, CHEN J, et al. Augmented dual-contrastive aggregation learning for unsupervised visible-infrared person re-identification[C]//Proceedings of the30th ACM International Conference on Multimedia, 2022: 2843-2851.

    [26] [26] WU Z, YE M. Unsupervised visible-infrared person re-identification via progressive graph matching and alternate learning[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 9548-9558.

    [27] [27] YANG B, CHEN J, YE M. Towards grand unified representation learning for unsupervised visible-infrared person re-identification[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023: 11069-11079.

    [28] [28] YANG B, CHEN J, CHEN C, et al. Dual Consistency-Constrained Learning for Unsupervised Visible-Infrared Person Re-Identification[J].IEEE Transactions on Information Forensics and Security, 2023(19): 1767-1779.

    [29] [29] CHEN Z, ZHANG Z, TAN X, et al. Unveiling the power of clip in unsupervised visible-infrared person re-identification[C]//Proceedings of the 31st ACM International Conference on Multimedia, 2023: 3667-3675.

    [30] [30] SHI J, YIN X, WANG Y, et al. Progressive Contrastive Learning with Multi-Prototype for Unsupervised Visible-Infrared Person Re-identification[J]. arXiv preprint arXiv: 2402.19026, 2024.

    [31] [31] Ester M, Kriegel H P, Sander J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]//KDD, 1996,96(34): 226-231.

    [32] [32] WU A, ZHENG W S, YU H X, et al. RGB-infrared cross-modality person re-identification[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017: 5380-5389.

    [33] [33] Nguyen D T, Hong H G, Kim K W, et al. Person recognition system based on a combination of body images from visible light and thermal cameras[J].Sensors, 2017,17(3): 605.

    [34] [34] YE M, SHEN J, LIN G, et al. Deep learning for person re-identification: A survey and outlook[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021,44(6): 2872-2893.

    [35] [35] DENG J, DONG W, Socher R, et al. Imagenet: A large-scale hierarchical image database[C]//2009IEEE conference on Computer Vision and Pattern Recognition. IEEE, 2009: 248-255.

    [36] [36] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.

    [37] [37] CHEN Y, WAN L, LI Z, et al. Neural feature search for rgb-infrared person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 587-597.

    [38] [38] KIM M, KIM S, Park J, et al. Partmix: Regularization strategy to learn part discovery for visible-infrared person re-identification[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023: 18621-18632.

    [39] [39] ZHANG Y, LU Y, YAN Y, et al. Frequency domain nuances mining for visible-infrared person re-identification[J]. arXiv preprint arXiv: 2401.02162, 2024.

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    ZHAO Hanshuo, MA Yiwen, ZHANG Yanxia, WANG Pei, YANG Jianwei. Soft-Weight Prototype Contrastive Learning for Unsupervised Visible-Infrared Person Re-Identification[J]. Infrared Technology, 2025, 47(5): 628

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    Paper Information

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    Received: May. 23, 2024

    Accepted: Jul. 3, 2025

    Published Online: Jul. 3, 2025

    The Author Email: YANG Jianwei (yangjianwei0913@gmail.com)

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