Graph convolution-based feature disentanglement for visible-infrared person re-identification

Ren Lou; Muyu Wang; Yihao Shen; Sanyuan Zhao; Xinyuan Wang; Yueqi Zhou; Fangfang Li; Qiangqiang Xiang

doi:10.1117/12.3049214

12 November 2024 Graph convolution-based feature disentanglement for visible-infrared person re-identification

Ren Lou, Muyu Wang, Yihao Shen, Sanyuan Zhao, Xinyuan Wang, Yueqi Zhou, Fangfang Li, Qiangqiang Xiang

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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 1339543 (2024) https://doi.org/10.1117/12.3049214
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China

Abstract

We propose a graph convolution-based disentanglement algorithm that is well-performed in the task of cross-modal person re-identification between visible and infrared images. Given the image of an individual in one modality, the problem to be addressed is whether the same person also appears in images from another modality. To tackle this issue, the main idea of our proposed method is to disentangle image features into modality-related and modality-invariant features, thereby alleviating feature discrepancies across different modal images. Unlike traditional disentanglement methods, our proposed graph convolution-based approach abandons the use of generative adversarial networks and employs attention mechanisms for initial disentanglement, followed by optimization of disentangled features using graph convolution. Comprehensive experimental results on the RegDB dataset and SYSU MM01 dataset demonstrate the superiority of our method in terms of effectiveness and efficiency.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Ren Lou, Muyu Wang, Yihao Shen, Sanyuan Zhao, Xinyuan Wang, Yueqi Zhou, Fangfang Li, and Qiangqiang Xiang "Graph convolution-based feature disentanglement for visible-infrared person re-identification", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 1339543 (12 November 2024); https://doi.org/10.1117/12.3049214

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