Source-Free Domain Adaptation with Frozen Multimodal Foundation Model
Authors: Song Tang, Wenxin Su, Mao Ye, Xiatian Zhu

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 pretrained 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.