Learning to Prompt for Vision-Language Models

TL;DR

This paper introduces CoOp, a simple learnable prompt method for vision-language models like CLIP, significantly reducing prompt engineering effort and improving downstream image recognition performance with minimal training data.

Contribution

Proposes Context Optimization (CoOp), a learnable prompt approach that adapts pre-trained vision-language models for various tasks without changing their parameters.

Findings

CoOp outperforms hand-crafted prompts with as few as one or two shots.

With 16 shots, CoOp achieves around 15% average gain over traditional prompts.

CoOp demonstrates strong domain generalization compared to zero-shot models.

Abstract

Large pre-trained vision-language models like CLIP have shown great potential in learning representations that are transferable across a wide range of downstream tasks. Different from the traditional representation learning that is based mostly on discretized labels, vision-language pre-training aligns images and texts in a common feature space, which allows zero-shot transfer to a downstream task via prompting, i.e., classification weights are synthesized from natural language describing classes of interest. In this work, we show that a major challenge for deploying such models in practice is prompt engineering, which requires domain expertise and is extremely time-consuming -- one needs to spend a significant amount of time on words tuning since a slight change in wording could have a huge impact on performance. Inspired by recent advances in prompt learning research in natural language processing (NLP), we propose Context Optimization (CoOp), a simple approach specifically for adapting CLIP-like vision-language models for downstream image recognition. Concretely, CoOp models a prompt's context words with learnable vectors while the entire pre-trained parameters are kept fixed. To handle different image recognition tasks, we provide two implementations of CoOp: unified context and class-specific context. Through extensive experiments on 11 datasets, we demonstrate that CoOp requires as few as one or two shots to beat hand-crafted prompts with a decent margin and is able to gain significant improvements over prompt engineering with more shots, e.g., with 16 shots the average gain is around 15% (with the highest reaching over 45%). Despite being a learning-based approach, CoOp achieves superb domain generalization performance compared with the zero-shot model using hand-crafted prompts.