@inproceedings{466e91eb40ce4520922330ec2d1c3617,
title = "Graph convolution-based feature disentanglement for visible-infrared person re-identification",
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.",
keywords = "Visible-infrared person re-identification, cross-modal, disentanglement, graph convolution",
author = "Ren Lou and Muyu Wang and Yihao Shen and Sanyuan Zhao and Xinyuan Wang and Yueqi Zhou and Fangfang Li and Qiangqiang Xiang",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; 2024 International Conference on Optics, Electronics, and Communication Engineering, OECE 2024 ; Conference date: 26-07-2024 Through 28-07-2024",
year = "2024",
doi = "10.1117/12.3049214",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Yang Yue",
booktitle = "International Conference on Optics, Electronics, and Communication Engineering, OECE 2024",
address = "United States",
}