Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging
visual learning tasks, which can be cast as a feature distribution matching problem. With the …

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 …

With the rapid development of the remote sensing monitoring and computer vision
technology, the deep learning method has made a great progress to achieve applications …

Detectors trained with massive labeled data often exhibit dramatic performance degradation
in some particular scenarios with data distribution gap. To alleviate this problem of domain …

Unsupervised domain adaptation (UDA) is to make predictions for unlabeled data on a
target domain, given labeled data on a source domain whose distribution shifts from the …

Open-set domain adaptation is a developing and practical solution to training deep networks
using unlabeled data which have been collected among unknown data and are under …

Despite the remarkable performance that modern deep neural networks have achieved on
independent and identically distributed (IID) data, they can crash under distribution shifts …

Abstract Domain generalization (DG) aims to improve the generalization performance for an
unseen target domain by using the knowledge of multiple seen source domains. Mainstream …

Domain adaptation (DA) aims to accomplish tasks on unlabeled target data by learning and
transferring knowledge from related source domains. In order to learn a discriminative and …

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