Due to privacy concerns and data transmission issues, Source-free Unsupervised Domain
Adaptation (SFDA) has gained popularity. It exploits pre-trained source models, rather than …

Deep learning techniques have achieved impressive progress in the task of person re-
identification. However, how to generalize a learned model from the source domain to the …

Unsupervised multi-source domain adaptation aims to obtain a model working well on the
unlabeled target domain by reducing the domain gap between the labeled source domains …

Cross-modal hashing has gained considerable attention in cross-modal retrieval due to its
low storage cost and prominent computational efficiency. However, preserving more …

Domain generalization (DG) aims to learn a model that generalizes well to an unseen test
distribution. Mainstream methods follow the domain-invariant representational learning …

Despite the great progress of unsupervised domain adaptation (UDA) with the deep neural
networks, current UDA models are opaque and cannot provide promising explanations …

Vision transformer has demonstrated great potential in abundant vision tasks. However, it
also inevitably suffers from poor generalization capability when the distribution shift occurs …

Distinguishing identity-unrelated background information from discriminative identity
information poses a challenge in unsupervised vehicle re-identification (Re-ID). Re-ID …

Unsupervised domain adaptation (UDA) person reidentification (Re-ID) aims to identify
pedestrian images within an unlabeled target domain with an auxiliary labeled source …

Vehicle re-identification (ReID) endeavors to associate vehicle images collected from a
distributed network of cameras spanning diverse traffic environments. This task assumes …