Propagation based Recycling Contrastive Learning for Coupled Noisy Visible-Infrared Person Re-identification

Visible-Infrared Person Re-Identification (VI-ReID) plays a crucial role in round-the-clock security surveillance systems, aiming to detect consistent identity recognition across transitions from day to night. A significant challenge in this field is the variation in the appearance of the same identity across visible and infrared modalities, which often leads to coupled noisy labels, referring to both Noisy Annotation (NA) and Noisy Correspondence (NC). Therefore, learning noisy-tolerant and discriminative representations is the primary objective in VI-ReID. However, existing research typically faces two principal limitations: (1) Learning strategies for noisy labeled scenarios usually rely on analyzing the distribution of loss response while ignoring the rich semantic information from neighboring samples. (2) When dealing with identified noisy samples, most previous approaches usually employ filtering strategies to mitigate the impact of noisy samples but fail to consider the valuable information in the noisy samples. To address these challenges, we propose a Propagation based Recycling Contrastive Learning (PRCL) approach. This method utilizes a label propagation strategy to distinguish clean annotations to learn identity-wise semantic information and recycles filtered noisy samples to capture the geometric-wise representation. Thus, even in the presence of noisy labels, the method can help learn robust representations across visible and infrared modalities. Specifically, we design a Noisy-aware Heterogeneous Graph Propagation module, which identifies noisy samples by aggregating the effects of neighboring labels using a graph propagation strategy. In addition, we develop a Cross Modality Recycling Debiased Contrastive Learning algorithm, which leverages the identity-wise information from clean samples and geometry-wise information from noisy samples. This approach utilizes identity-wise and geometric-wise information to mitigate the effect of noisy labels and retain as much valuable information as possible. Extensive experiments on two VI-ReID benchmark datasets demonstrate that our proposed method achieves highly competitive performance.