Underrepresented in Foundation Model Pretraining Data? A One-Shot Probe

TL;DR

This paper introduces a data-efficient, one-shot probing method using large language models to predict the zero-shot accuracy of vision-language foundation models across diverse and underrepresented visual domains, aiding resource allocation.

Contribution

It presents a novel, low-cost approach to estimate VLFM performance in niche domains with minimal data, especially benefiting underrepresented regions like Africa.

Findings

Achieves a Pearson-r correlation of 0.96 in accuracy prediction

Effective across five diverse datasets including underrepresented domains

Provides a practical tool for informed decision-making in model deployment

Abstract

Large-scale Vision-Language Foundation Models (VLFMs), such as CLIP, now underpin a wide range of computer vision research and applications. VLFMs are often adapted to various domain-specific tasks. However, VLFM performance on novel, specialised, or underrepresented domains remains inconsistent. Evaluating VLFMs typically requires labelled test sets, which are often unavailable for niche domains of interest, particularly those from the Global South. We address this gap by proposing a highly data-efficient method to predict a VLFM's zero-shot accuracy on a target domain using only a single labelled image per class. Our approach uses a Large Language Model to generate plausible counterfactual descriptions of a given image. By measuring the VLFM's ability to distinguish the correct description from these hard negatives, we engineer features that capture the VLFM's discriminative power in its shared embedding space. A linear regressor trained on these similarity scores estimates the VLFM's zero-shot test accuracy across various visual domains with a Pearson-r correlation of 0.96. We demonstrate our method's performance across five diverse datasets, including standard benchmark datasets and underrepresented datasets from Africa. Our work provides a low-cost, reliable tool for probing VLFMs, enabling researchers and practitioners to make informed decisions about data annotation efforts before committing significant resources. The model training code, generated captions and counterfactuals are released here: https://github.com/chris-vorster/PreLabellingProbe.