Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

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

The domain generalization approach seeks to develop a universal model that performs well
on unknown target domains with the aid of diverse source domains. Data augmentation has …

We investigate the generalization boundaries of current Multimodal Large Language Models
(MLLMs) via comprehensive evaluation under out-of-distribution scenarios and domain …

Prompt learning has been widely adopted to efficiently adapt vision-language models
(VLMs), eg. CLIP, for few-shot image classification. Despite their success, most prompt …

Abstract Source-Free Domain Adaptation (SFDA) aims to adapt a source model for a target
domain with only access to unlabeled target training data and the source model pretrained …

Stylized text-to-image generation focuses on creating images from textual descriptions while
adhering to a style specified by reference images. However, subtle style variations within …

Source-Free Domain Generalization (SFDG) aims to develop a model that works for unseen
target domains without relying on any source domain. Research in SFDG primarily bulids …

Over the past year a large body of multimodal research has emerged around zero-shot
evaluation using GPT descriptors. These studies boost the zero-shot accuracy of pretrained …

To avoid the high cost of collecting visual data from all test domains in the domain
adaptation task, recent work takes advantage of the pre-trained large-scale vision language …