  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 pre-trained on a supervised source domain. Relying on pseudo
labeling and/or auxiliary supervision, conventional methods are inevitably
error-prone. To mitigate this limitation, in this work we for the first time
explore the potentials of off-the-shelf vision-language (ViL) multimodal models
(e.g.,CLIP) with rich whilst heterogeneous knowledge. We find that directly
applying the ViL model to the target domain in a zero-shot fashion is
unsatisfactory, as it is not specialized for this particular task but largely
generic. To make it task specific, we propose a novel Distilling multimodal
Foundation model(DIFO)approach. Specifically, DIFO alternates between two steps
during adaptation: (i) Customizing the ViL model by maximizing the mutual
information with the target model in a prompt learning manner, (ii) Distilling
the knowledge of this customized ViL model to the target model. For more
fine-grained and reliable distillation, we further introduce two effective
regularization terms, namely most-likely category encouragement and predictive
consistency. Extensive experiments show that DIFO significantly outperforms the
state-of-the-art alternatives. Code is here
