Contrastive learning enhanced pseudo-labeling for unsupervised domain adaptation in person re-identification.

ABSTRACT: Person re-identification (ReID) technology has many applications in intelligent surveillance and public safety. However, the domain difference between the source and target domains makes the generalization ability of the model extremely challenging. To reduce the dependence on labeled data, Unsupervised Domain Adaptation (UDA) methods have become an effective way to solve this problem. However, the influence of pseudo-label generated noise on model training in existing UDA methods is still significant, resulting in limited model performance on the target domain. For this reason, this paper proposes a contrast learning-based pseudo-label refinement with probabilistic uncertainty in the unsupervised domain, adapted to Person re-identification, aiming to improve the effectiveness of the unsup

SUBMITTER: Bai X 

PROVIDER: S-EPMC12258581 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature