SiCL: Silhouette-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes Change

arXiv:2305.13600v2 Announce Type: replace
Abstract: In this paper, we address a highly challenging yet critical task: unsupervised long-term person re-identification with clothes change. Existing unsupervised person re-id methods are mainly designed for short-term scenarios and usually rely on RGB cues so that fail to perceive feature patterns that are independent of the clothes. To crack this bottleneck, we propose a silhouette-driven contrastive learning (SiCL) method, which is designed to learn cross-clothes invariance by integrating both the RGB cues and the …

abstract arxiv change cs.cv feature identification long-term paper patterns person type unsupervised