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Learning a Robust Synthetic Modality with Dual-Level Alignment for Visible-Infrared Person Re-identification

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Pattern Recognition and Computer Vision (PRCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15035))

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Abstract

Visible-Infrared Person Re-identification (VI-ReID) is a challenging task that involves matching visible and infrared person images across multiple camera views. The huge gap between visible and infrared modalities has become a significant bottleneck. Existing works typically employ dual-stream networks to extract shared modality representation, yet struggle to relieve such gap, resulting in inferior performance. To overcome this issue, we propose a robust synthetic modality learning with a dual-level alignment method (RSDL) for VI-ReID that aims to generate a robust synthetic modality as a bridge to guide cross-modality alignment. Specifically, the hetero-modality fusion (HMF) strategy is introduced to generate the robust synthetic modality by using multi-scale feature fusion with a structure rebuild module (SRM) and a cross-modality spatial alignment (CSA) module. The strategy incorporates rich semantic structural patterns from visible and infrared images to handle the modality variation. Additionally, we design the dual-level regulation loss to jointly explore the stable feature relationships among three modalities at both the instance and distribution levels for cross-modality alignment. This facilitates discovering modality-consistent and identity-aware representations. Extensive experiments on three VI-ReID benchmarks demonstrate the effectiveness of our proposed method.

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Acknowledgements

This study was partly supported by the National Natural Science Foundation of China (Grant No. 61802058, 62306061).

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Authors and Affiliations

  1. School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China

    Zichun Wang & Xu Cheng

Authors
  1. Zichun Wang
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  2. Xu Cheng
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Corresponding author

Correspondence to Xu Cheng .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Zhouchen Lin

  2. Nankai University, Tianjin, China

    Ming-Ming Cheng

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Ran He

  4. Xinjiang University, Ürümqi, Xinjiang, China

    Kurban Ubul

  5. Xinjiang University, Ürümqi, China

    Wushouer Silamu

  6. Peking University, Beijing, China

    Hongbin Zha

  7. Tsinghua University, Beijing, China

    Jie Zhou

  8. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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Wang, Z., Cheng, X. (2025). Learning a Robust Synthetic Modality with Dual-Level Alignment for Visible-Infrared Person Re-identification. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15035. Springer, Singapore. https://doi.org/10.1007/978-981-97-8620-6_20

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  • DOI: https://doi.org/10.1007/978-981-97-8620-6_20

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  • Online ISBN: 978-981-97-8620-6

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