Article Dans Une Revue ACM Transactions on Multimedia Computing, Communications and Applications Année : 2025

Visible-Infrared Person Re-Identification Based on Feature Decoupling and Refinement

Résumé

The objective of visible-infrared person re-identification is to accurately match pedestrian images captured in different modalities. Since these images are taken from varying viewpoints by different cameras, the cross-modal detection task must address both modality discrepancies and camera variations. Many existing approaches primarily focus on minimizing inter-modality differences to enhance retrieval accuracy, often overlooking the impact of camera viewpoint differences. To tackle these challenges, this article introduces a hierarchical feature decoupling network. First, the network decouples and extracts camera-related and camera-irrelated features separately to mitigate the effects of camera variations. Second, it addresses modality differences by extracting modality-independent features. Additionally, an adversarial decoupling loss is employed to further disentangle identity-irrelevant information from identity-relevant features, thereby boosting the system’s accuracy and robustness. Extensive experiments conducted on the SYSU-MM01 and RegDB datasets validate the effectiveness of the proposed method.

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Dates et versions

hal-05263785 , version 1 (16-09-2025)

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Citer

Hao Ding, Jing Sun, Rui Long, Xiaoping Jiang, Hongling Shi, et al.. Visible-Infrared Person Re-Identification Based on Feature Decoupling and Refinement. ACM Transactions on Multimedia Computing, Communications and Applications, 2025, 21 (9), pp.1-16. ⟨10.1145/3749843⟩. ⟨hal-05263785⟩
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