Negative Learning and Dual Contrastive for Unsupervised Visible-Infrared Person Re-identification

Abstract

Unsupervised visible-infrared person re-identification (US-VI-ReID) aims to identify target person images from different modalities without requiring annotations. Existing works generally learn modality-invariant features by using pseudo-labels. However, the inherent noise in these labels misleads the network’s training. Besides, these methods neglect more fine-grained information. To address these issues, we proposed a Negative Learning and Dual Contrastive (NLDC) framework to efficiently learn invariant feature representations. Specifically, we introduced a cross-modal negative learning method with complementary labels to robustly handle label noise. We also designed a low-similarity label selection strategy to construct reliable complementary label sets, which support the negative learning process. Additionally, we introduce both intra-modality and inter-modality instance contrastive losses to achieve fine-grained feature alignment. Experimental results on the SYSU-MM01 and RegDB datasets demonstrate the effectiveness of our proposed method, achieving competitive performance.