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<p>Visible-infrared person re-identification (VI-ReID) aims to match pedestrian identities across heterogeneous modalities with substantial cross-modal discrepancy. Most existing approaches attempt to reduce the gap via direct feature alignment, which may distort modality-specific semantics and lead to inconsistent representations. To address these limitations, we propose a Collaborative Calibration and Bridging Network (CCBNet) for VI-ReID. The proposed framework is composed of three complementary components. First, we introduce a Global–Local Channel Augmentation (GLCA) strategy to mitigate modality-specific color bias by applying channel exchange at both global and local scales, improving cross-modal robustness while preserving semantic structure. Second, we design an Adaptive Modal Alignment Bridging (AMAB) module that constructs an intermediate representation between the visible and infrared modalities through lightweight cross-modal interactions, enabling a smoother semantic transition across modalities. Third, we propose a Modality Bridge Distribution Consistency (MBDC) loss, which facilitates refined optimization of the intermediate-modality features, enhancing their accuracy and robustness while promoting coherent alignment across visible, infrared, and intermediate representations. Extensive experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate that the proposed CCBNet consistently outperforms state-of-the-art VI-ReID methods. The related code is available at <a href="https://github.com/sereinlll/CCBNet">https://github.com/sereinlll/CCBNet</a>.</p>

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CCBNet: collaborative calibration and bridging network for visible-infrared person re-identification

  • 期刊论文

  • 发表日期：

    2026年5月20日

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  • Wen Gao,
  • Lei Dai,
  • Zhihua Chen

摘要

Visible-infrared person re-identification (VI-ReID) aims to match pedestrian identities across heterogeneous modalities with substantial cross-modal discrepancy. Most existing approaches attempt to reduce the gap via direct feature alignment, which may distort modality-specific semantics and lead to inconsistent representations. To address these limitations, we propose a Collaborative Calibration and Bridging Network (CCBNet) for VI-ReID. The proposed framework is composed of three complementary components. First, we introduce a Global–Local Channel Augmentation (GLCA) strategy to mitigate modality-specific color bias by applying channel exchange at both global and local scales, improving cross-modal robustness while preserving semantic structure. Second, we design an Adaptive Modal Alignment Bridging (AMAB) module that constructs an intermediate representation between the visible and infrared modalities through lightweight cross-modal interactions, enabling a smoother semantic transition across modalities. Third, we propose a Modality Bridge Distribution Consistency (MBDC) loss, which facilitates refined optimization of the intermediate-modality features, enhancing their accuracy and robustness while promoting coherent alignment across visible, infrared, and intermediate representations. Extensive experiments conducted on the SYSU-MM01 and RegDB datasets demonstrate that the proposed CCBNet consistently outperforms state-of-the-art VI-ReID methods. The related code is available at https://github.com/sereinlll/CCBNet.