As AI systems have obtained significant performance to be deployed widely in our daily lives
and human society, people both enjoy the benefits brought by these technologies and suffer …

Adversarial learning has achieved remarkable performances for unsupervised domain
adaptation (UDA). Existing adversarial UDA methods typically adopt an additional …

Conventional machine learning relies on two presumptions:(1) the training and testing
datasets follow the same independent distribution, and (2) an adequate quantity of samples …

Unsupervised domain adaptation (UDA) aims to transfer knowledge learned from a labeled
source domain to a different unlabeled target domain. Most existing UDA methods focus on …

Unsupervised domain adaptation (UDA) aims to adapt models learned from a well-
annotated source domain to a target domain, where only unlabeled samples are given …

Endeavors have been recently made to leverage the vision transformer (ViT) for the
challenging unsupervised domain adaptation (UDA) task. They typically adopt the cross …

Abstract Deep Neural Networks have exhibited considerable success in various visual tasks.
However, when applied to unseen test datasets, state-of-the-art models often suffer …

Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled
source domain to an unlabeled target domain. Typically, to guarantee desirable knowledge …

The performance of nighttime semantic segmentation is restricted by the poor illumination
and a lack of pixel-wise annotation, which severely limit its application in autonomous …

Abstract Domain Adaptation (DA) is always challenged by the spurious correlation between
the domain-invariant features (eg, class identity) and the domain-specific ones (eg …