  Unsupervised domain adaption (UDA) has emerged as a popular solution to
tackle the divergence between the labeled source and unlabeled target domains.
Recently, some research efforts have been made to leverage large
vision-language models, such as CLIP, and then fine-tune or learn prompts from
them for addressing the challenging UDA task. In this work, we shift the gear
to a new direction by directly leveraging CLIP to measure the domain divergence
and propose a novel language-guided approach for UDA, dubbed as CLIP-Div. Our
key idea is to harness CLIP to 1) measure the domain divergence via the
acquired domain-agnostic distribution and 2) calibrate the target pseudo labels
with language guidance, to effectively reduce the domain gap and improve the
UDA model's generalization capability. Specifically, our major technical
contribution lies in the proposed two novel language-guided domain divergence
measurement losses: absolute divergence and relative divergence. These loss
terms furnish precise guidelines for aligning the distributions of the source
and target domains with the domain-agnostic distribution derived from CLIP.
Additionally, we propose a language-guided pseudo-labeling strategy for
calibrating the target pseudo labels. Buttressed by it, we show that a further
implementation for self-training can enhance the UDA model's generalization
capability on the target domain. CLIP-Div surpasses state-of-the-art CNN-based
methods by a substantial margin, achieving a performance boost of +10.3% on
Office-Home, +1.5% on Office-31, +0.2% on VisDA-2017, and +24.3% on DomainNet,
respectively.
