Cooperative Separation of Modality Shared-Specific Features for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification (VI-ReID) is a challenging task because the different imaging principles of visible and infrared images bring about huge modality discrepancy. Existing methods primarily address this issue by generating intermediate images to align modality features and esta...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2024, Vol.26, p.8172-8183
Main Authors: Yang, Xi, Dong, Wenjiao, Li, Meijie, Wei, Ziyu, Wang, Nannan, Gao, Xinbo
Format: Article
Language:English
Subjects:
cooperative separation
Feature extraction
Generative adversarial networks
Generators
Image color analysis
Pedestrians
saliency response
Task analysis
Training
Visible-infrared person re-identification (VI-ReID)
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Summary:Visible-infrared person re-identification (VI-ReID) is a challenging task because the different imaging principles of visible and infrared images bring about huge modality discrepancy. Existing methods primarily address this issue by generating intermediate images to align modality features and establish connections between the visible and infrared modalities. However, the quality of these generated images is often unstable, limiting the effectiveness of such approaches. To overcome this limitation, we propose a novel method called modality shared-specific features cooperative separation. It consists of two key modules: the saliency response module and the cooperative separation module, aimed at alleviating the modality gap. The saliency response module incorporates a location attention mechanism and local features to construct contextual connections and extract local saliency information. Then, the cooperative separation module employs a more concise dual-MLPs as generator to effectively separate shared-specific features. Additionally, we introduce a shared feature refinement mechanism in both the generator and discriminator. By coordinating the shared-specific features, our method achieves secondary separation and extracts purer modality-shared features without specific information. Extensive experiments conducted on the SYSU-MM01 and RegDB public datasets demonstrate that our proposed method performs excellently in VI-ReID.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2024.3377139