Will It Zero-Shot?: Predicting Zero-Shot Classification Performance For Arbitrary Queries

• URL: http://arxiv.org/abs/2601.17535v2
• Date: Tue, 27 Jan 2026 18:04:35 GMT
• Title: Will It Zero-Shot?: Predicting Zero-Shot Classification Performance For Arbitrary Queries
• Authors: Kevin Robbins, Xiaotong Liu, Yu Wu, Le Sun, Grady McPeak, Abby Stylianou, Robert Pless,
• Abstract summary: We build on prior work using text-only comparisons to evaluate how well a model works for a given natural language task.<n>We explore approaches that also generate synthetic images relevant to that task to evaluate and refine the prediction of zero-shot accuracy.
• Score: 19.511404894563455
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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