Visible-infrared person re-identification (VI-ReID) retrieves cross-modal identity matches between visible and infrared images, offering significant value for round-the-clock surveillance. Despite recent advances, challenges remain: the task relies heavily on high-quality annotations, and factors such as occlusion, viewpoint variations, and the inherent difficulty of labeling infrared images inevitably introduce noisy annotations (NA) into the dataset during large-scale dataset construction. Moreover, coupled noisy labels in two modalities lead to noisy correspondence (NC), further complicating the learning process. Although prior research has achieved relatively stable results in addressing the NA and NC problem for VI-ReID through noise detection and robust loss functions, they still exhibit certain limitations: 1) Underutilization of training data. Existing methods often discard noisy samples to mitigate their negative impact, overlooking their potential value. 2) Lack of historical relevance. Unstable learning dynamics under noisy labels lead to inconsistent outputs, yet current approaches ignore the valuable historical information embedded in these fluctuations. Focusing on these challenges in VI-ReID, we propose Self-Rectification Historical Consistency Learning (SRHCL) for VI-ReID, which consists of noise detection, self-refined label rectification, and historical consistency learning modules. Firstly, the noise detection module calculates confidence weights for each sample by modeling the model’s loss response, thereby mitigating the adverse impact of noisy samples in subsequent training phases. Secondly, we propose a self-refined label rectification module to rectify noisy labels by reliable historical predictions, progressively collating the training data at fixed intervals. Finally, we introduce cross-modal contrastive learning and early learning regularization based on momentum-updated memories to facilitate historical consistency learning. Extensive experiments conducted on SYSU-MM01 and RegDB datasets demonstrate the robustness and effectiveness of our method across varying noisy ratios.

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.

The person re-identification task aims to retrieve the same identity under different cameras. The main difficulties of the task lie in the collection of a large amount of annotated data and the diversity of pedestrians. Therefore, how to learn a robust and discriminative representation feature with unlabeled data is the key to this task. The pseudo label based methods have shown significant effectiveness in the field by generating pseudo labels from unlabeled data instead of ground-truth labels. However, existing researches typically suffer two limitations: (1) The extracted features are insufficient to reflect the subtle local semantics; (2) The pseudo labels generated by clustering methods cannot avoid introducing noise, which will seriously affect the performance of the discriminative feature. In this paper, to address the above problems, we propose a Distribution-Guided Hierarchical Calibration Contrastive Network (DHCCN) to better exploit local clues and hierarchical representation, which can consider cross-granularity consistency and reduce the noise of pseudo labels by the calibrated feature distribution. A Hierarchical Feature Extractor is employed to capture the multi-granularity response of each image, and fuse both global salience and local subtle texture information of a pedestrian to generate the hierarchical feature. In addition, to reduce the error of the pseudo labels, we introduce a Feature Distribution Corrector to calibrate noisy features of low-confidence samples evaluated by a Gaussian Mixture Model. At last, we integrate cross-granularity consistency constraint by the difference between the global and local feature, which can help generate more accurate feature embedding and improve robustness of the model. Therefore, we can receive a performance that is close to the supervised person re-identification task by narrowing the gap between the pseudo and ground-truth label. Experiments on four standard benchmarks demonstrate the effectiveness of our method against the state-of-the-art unsupervised re-identification methods. The code is available at https://github.com/Li-Yongxi/2023-DHCCN.