Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification

• URL: http://arxiv.org/abs/2402.19026v2
• Date: Mon, 27 May 2024 02:45:21 GMT
• Title: Learning Commonality, Divergence and Variety for Unsupervised Visible-Infrared Person Re-identification
• Authors: Jiangming Shi, Xiangbo Yin, Yaoxing Wang, Xiaofeng Liu, Yuan Xie, Yanyun Qu,
• Abstract summary: Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match specified people in infrared images to visible images without annotation, and vice versa. Most existing methods address the USVI-ReID problem using cluster-based contrastive learning. We propose a Progressive Contrastive Learning with Multi-Prototype (PCLMP) method for USVI-ReID.
• Score: 24.509648608359296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match specified people in infrared images to visible images without annotation, and vice versa. USVI-ReID is a challenging yet under-explored task. Most existing methods address the USVI-ReID problem using cluster-based contrastive learning, which simply employs the cluster center as a representation of a person. However, the cluster center primarily focuses on shared information, overlooking disparity. To address the problem, we propose a Progressive Contrastive Learning with Multi-Prototype (PCLMP) method for USVI-ReID. In brief, we first generate the hard prototype by selecting the sample with the maximum distance from the cluster center. This hard prototype is used in the contrastive loss to emphasize disparity. Additionally, instead of rigidly aligning query images to a specific prototype, we generate the dynamic prototype by randomly picking samples within a cluster. This dynamic prototype is used to retain the natural variety of features while reducing instability in the simultaneous learning of both common and disparate information. Finally, we introduce a progressive learning strategy to gradually shift the model's attention towards hard samples, avoiding cluster deterioration. Extensive experiments conducted on the publicly available SYSU-MM01 and RegDB datasets validate the effectiveness of the proposed method. PCLMP outperforms the existing state-of-the-art method with an average mAP improvement of 3.9%. The source codes will be released.

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