Will It Zero-Shot?: Predicting Zero-Shot Classification Performance For Arbitrary Queries
This work helps non-expert users assess VLM effectiveness for their applications without labeled data, though it is incremental by building on prior text-only methods.
The paper tackles the problem of predicting zero-shot classification performance for vision-language models (VLMs) like CLIP, showing that using generated synthetic images alongside text comparisons improves prediction accuracy compared to text-only baselines.
Vision-Language Models like CLIP create aligned embedding spaces for text and images, making it possible for anyone to build a visual classifier by simply naming the classes they want to distinguish. However, a model that works well in one domain may fail in another, and non-expert users have no straightforward way to assess whether their chosen VLM will work on their problem. We build on prior work using text-only comparisons to evaluate how well a model works for a given natural language task, and explore approaches that also generate synthetic images relevant to that task to evaluate and refine the prediction of zero-shot accuracy. We show that generated imagery to the baseline text-only scores substantially improves the quality of these predictions. Additionally, it gives a user feedback on the kinds of images that were used to make the assessment. Experiments on standard CLIP benchmark datasets demonstrate that the image-based approach helps users predict, without any labeled examples, whether a VLM will be effective for their application.