Disentangled Sample Guidance Learning for Unsupervised Person Re-Identification

Unsupervised person re-identification (Re-ID) is challenging due to the lack of ground truth labels. Most existing methods employ iterative clustering to generate pseudo labels for unlabeled training data to guide the learning process. However, how to select samples that are both associated with high-confidence pseudo labels and hard (discriminative) enough remains a critical problem. To address this issue, a disentangled sample guidance learning (DSGL) method is proposed for unsupervised Re-ID. The method consists of disentangled sample mining (DSM) and discriminative feature learning (DFL). DSM disentangles (unlabeled) person images into identity-relevant and identity-irrelevant factors, which are used to construct disentangled positive/negative groups that contain discriminative enough information. DFL incorporates the mined disentangled sample groups into model training by a surrogate disentangled learning loss and a disentangled second-order similarity regularization, to help the model better distinguish the characteristics of different persons. By using the DSGL training strategy, the mAP on Market-1501 and MSMT17 increases by 6.6% and 10.1% when applying the ResNet50 framework, and by 0.6% and 6.9% with the vision transformer (VIT) framework, respectively, validating the effectiveness of the DSGL method. Moreover, DSGL surpasses previous state-of-the-art methods by achieving higher Top-1 accuracy and mAP on the Market-1501, MSMT17, PersonX, and VeRi-776 datasets. The source code for this paper is available at https://github.com/jihaoxuanye/DiseSGL .