MaskCL: Semantic Mask-Driven Contrastive Learning for Unsupervised Person Re-Identification with Clothes Change
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This paper considers a novel and challenging problem: unsupervised long-term person re-identification with clothes change. Unfortunately, conventional unsupervised person re-id methods are designed for short-term cases and thus fail to perceive clothes-independent patterns due to simply being driven by RGB prompt. To tackle with such a bottleneck, we propose a semantic mask-driven contrastive learning approach, in which silhouette masks are embedded into contrastive learning framework as the semantic prompts and cross-clothes invariance is learnt from hierarchically semantic neighbor structure by combining both RGB and semantic features in a two-branches network. Since such a challenging re-id task setting is investigated for the first time, we conducted extensive experiments to evaluate state-of-the-art unsupervised short-term person re-id methods on five widely-used clothes-change re-id datasets. Experimental results verify that our approach outperforms the unsupervised re-id competitors by a clear margin, remaining a narrow gap to the supervised baselines.
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