Video analysis tasks such as action recognition have received increasing research interest
with growing applications in fields such as smart healthcare, thanks to the introduction of …

Abstract Domain generalization studies the problem of training a model with samples from
several domains (or distributions) and then testing the model with samples from a new …

Abstract Conventional Unsupervised Domain Adaptation (UDA) strives to minimize
distribution discrepancy between domains which neglects to harness rich semantics from …

Contrastive language-image pre-trained (CLIP) models have zero-shot ability of classifying
an image belonging to “[CLASS]” by using similarity between the image and the prompt …

The zero-shot performance of existing vision-language models (VLMs) such as CLIP is
limited by the availability of large-scale aligned image and text datasets in specific domains …

Large-scale pre-trained Vision-Language Models (VLMs), such as CLIP and ALIGN, have
introduced a new paradigm for learning transferable visual representations. Recently, there …

Large pre-trained vision language models (VLMs) have shown impressive zero-shot ability
on downstream tasks with manually designed prompt. To further adapt VLMs to downstream …

Abstract Domain Generalization (DG) aims to develop a versatile model capable of
performing well on unseen target domains. Recent advancements in pre-trained Visual …

Multi-Source Domain Adaptation (MSDA) aims to mitigate changes in data distribution when
transferring knowledge from multiple labeled source domains to an unlabeled target …

In this paper, we aim to adapt a model at test-time using a few unlabeled data to address
distribution shifts. To tackle the challenges of extracting domain knowledge from a limited …