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Progressive Contrastive Learning with Multi-Prototype for Unsupervised Visible-Infrared Person Re-identification
March 1, 2024, 5:47 a.m. | Jiangming Shi, Xiangbo Yin, Yaoxing Wang, Xiaofeng Liu, Yuan Xie, Yanyun Qu
cs.CV updates on arXiv.org arxiv.org
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) …
abstract annotation arxiv center cluster cs.cv identification images match people person type unsupervised
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