Vision models excel in image classification but struggle to generalize to unseen data, such
as classifying images from unseen domains or discovering novel categories. In this paper …

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 …

In machine learning, generalization against distribution shifts--where deployment conditions
diverge from the training scenarios--is crucial, particularly in fields like climate modeling …

This paper addresses a machine learning problem often challenged by differences in the
distributions of training and real-world data. We propose a framework that addresses the …

Abstract Domain generalization aims to solve the challenge of Out-of-Distribution (OOD)
generalization by leveraging common knowledge learned from multiple training domains to …