  Prompt learning has emerged as a valuable technique in enhancing
vision-language models (VLMs) such as CLIP for downstream tasks in specific
domains. Existing work mainly focuses on designing various learning forms of
prompts, neglecting the potential of prompts as effective distillers for
learning from larger teacher models. In this paper, we introduce an
unsupervised domain prompt distillation framework, which aims to transfer the
knowledge of a larger teacher model to a lightweight target model through
prompt-driven imitation using unlabeled domain images. Specifically, our
framework consists of two distinct stages. In the initial stage, we pre-train a
large CLIP teacher model using domain (few-shot) labels. After pre-training, we
leverage the unique decoupled-modality characteristics of CLIP by pre-computing
and storing the text features as class vectors only once through the teacher
text encoder. In the subsequent stage, the stored class vectors are shared
across teacher and student image encoders for calculating the predicted logits.
Further, we align the logits of both the teacher and student models via KL
divergence, encouraging the student image encoder to generate similar
probability distributions to the teacher through the learnable prompts. The
proposed prompt distillation process eliminates the reliance on labeled data,
enabling the algorithm to leverage a vast amount of unlabeled images within the
domain. Finally, the well-trained student image encoders and pre-stored text
features (class vectors) are utilized for inference. To our best knowledge, we
are the first to (1) perform unsupervised domain-specific prompt-driven
knowledge distillation for CLIP, and (2) establish a practical pre-storing
mechanism of text features as shared class vectors between teacher and student.
Extensive experiments on 11 datasets demonstrate the effectiveness of our
method.
