Latent Class Inference for Calibrated Vision-Language Models in Few-Shot OOD Learning

Abstract

Abstract: In the realm of few-shot learning, foundation models like CLIP have proven effective but exhibit limitations in cross-domain robustness especially in few-shot settings. Recent works add text as an extra modality to enhance the performance of these models. Most of these approaches treat text as an auxiliary modality without fully exploring its potential to elucidate the underlying class visual features distribution. In this paper, we present a novel approach that leverages text-derived statistics to predict the mean and covariance of the visual feature distribution for each class. This predicti

Research Question

To what extent does inferring latent class distributions from text descriptions improve the calibration and accuracy of vision-language models in out-of-distribution few-shot learning scenarios relative to prompt-based baselines?

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