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Modality Synergy Complement Learning with Cascaded Aggregation for Visible-Infrared Person Re-Identification

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Book cover Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13674))

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Abstract

Visible-Infrared Re-Identification (VI-ReID) is challenging in image retrievals. The modality discrepancy will easily make huge intra-class variations. Most existing methods either bridge different modalities through modality-invariance or generate the intermediate modality for better performance. Differently, this paper proposes a novel framework, named Modality Synergy Complement Learning Network (MSCLNet) with Cascaded Aggregation. Its basic idea is to synergize two modalities to construct diverse representations of identity-discriminative semantics and less noise. Then, we complement synergistic representations under the advantages of the two modalities. Furthermore, we propose the Cascaded Aggregation strategy for fine-grained optimization of the feature distribution, which progressively aggregates feature embeddings from the subclass, intra-class, and inter-class. Extensive experiments on SYSU-MM01 and RegDB datasets show that MSCLNet outperforms the state-of-the-art by a large margin. On the large-scale SYSU-MM01 dataset, our model can achieve 76.99% and 71.64% in terms of Rank-1 accuracy and mAP value. Our code will be available at https://github.com/bitreidgroup/VI-ReID-MSCLNet.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61902027, and the Start-up Research Grant (SRG) of University of Macau.

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Authors and Affiliations

  1. School of Computer Science, Beijing Institute of Technology, Beijing, China

    Yiyuan Zhang, Sanyuan Zhao, Yuhao Kang & Jianbing Shen

  2. SKL-IOTSC, Department of Computer and Information Science, University of Macau, Taipa, Macau

    Jianbing Shen

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  1. Yiyuan Zhang
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  2. Sanyuan Zhao
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  3. Yuhao Kang
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  4. Jianbing Shen
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Corresponding author

Correspondence to Sanyuan Zhao .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Zhang, Y., Zhao, S., Kang, Y., Shen, J. (2022). Modality Synergy Complement Learning with Cascaded Aggregation for Visible-Infrared Person Re-Identification. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13674. Springer, Cham. https://doi.org/10.1007/978-3-031-19781-9_27

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  • DOI: https://doi.org/10.1007/978-3-031-19781-9_27

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