Pre-trained vision-language models (VLMs) have shown impressive results in various visual
classification tasks. However, we often fail to fully unleash their potential when adapting …

Recent advances in vision-language foundational models, such as CLIP, have
demonstrated significant strides in zero-shot classification. However, the extensive …

Abstract Unsupervised Domain Adaptation excels in transferring predictive models from a
labeled source domain to an unlabeled target domain. However, acquiring sufficient source …

Large pre-trained vision language models (VLMs) have demonstrated impressive
representation learning capabilities, but their transferability across various downstream …

Recent advancements in prompt tuning have enhanced the adaptation of large pre-trained
models to target tasks. However, existing methods struggle to establish an effective balance …

In realistic open-set scenarios where labels of a part of testing data are totally unknown,
when vision-language (VL) prompt learning methods encounter inputs related to unknown …