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Abstract Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to match person images across visible and infrared modalities without any labeled data, which is severely hindered by the large cross-modality discrepancy and the absence of ground-truth annotations. Recent advances predominantly adopt unsupervised contrastive learning frameworks that rely on clustering-generated pseudo-labels to guide representation learning.
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