2025-01-14-lin25a.md

title	Visible-Infrared Person Re-Indentification via Feature Fusion and Deep Mutual Learning
booktitle	Proceedings of the 16th Asian Conference on Machine Learning
year	2025
volume	260
series	Proceedings of Machine Learning Research
month	0
publisher	PMLR
pdf	https://raw.githubusercontent.com/mlresearch/v260/main/assets/lin25a/lin25a.pdf
url	https://proceedings.mlr.press/v260/lin25a.html
openreview	NmoEsT5Rul
abstract	Visible-Infrared Person Re-Identification (VI-ReID) aims to retrieve a set of person images captured from both visible and infrared camera views. Addressing the challenge of modal differences between visible and infrared images, we propose a VI-ReID network based on Feature Fusion and Deep Mutual Learning (DML). To enhance the model’s robustness to color, we introduce a novel data augmentation method called Random Combination of Channels (RCC), which generates new images by randomly combining R, G, and B channels of visible images. Furthermore, to capture more informative features of individuals, we fuse the features from the middle layer of the network. To reduce the model’s dependence on global features, we employ a fusion branch as an auxiliary branch, facilitating synchronous learning of global and fusion branches through Deep Mutual Learning . Extensive experiments on the SYSU-MM01 and RegDB datasets validate the superiority of our method, showcasing its excellent performance when compared to other state-of-the-art approaches.
layout	inproceedings
issn	2640-3498
id	lin25a
tex_title	Visible-Infrared Person Re-Indentification via Feature Fusion and Deep Mutual Learning
firstpage	79
lastpage	94
page	79-94
order	79
cycles	false
bibtex_editor	Nguyen, Vu and Lin, Hsuan-Tien
editor	given family Vu Nguyen given family Hsuan-Tien Lin
bibtex_author	Lin, Ziyang and Wang, Banghai
author	given family Ziyang Lin given family Banghai Wang
date	2025-01-14
address
container-title	Proceedings of the 16th Asian Conference on Machine Learning
genre	inproceedings
issued	date-parts 2025 1 14
extras