PromptKD: Unsupervised Prompt Distillation for Vision-Language Models

Zheng Li, Xiang Li, Xinyi Fu, Xin Zhang, Weiqiang Wang, Shuo Chen, Jian Yang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 26617-26626

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. Code is publicly available at https://github.com/zhengli97/PromptKD.