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 predictive framework enriches the latent space, yielding more robust and generalizable few-shot learning models. We demonstrate the efficacy of incorporating both mean and covariance statistics in improving few-shot classification performance across various datasets. Our method shows that we can use text to predict the mean and covariance of the distribution offering promising improvements in few-shot learning scenarios.
@article{arxiv.2311.14544,
title = {Inferring Latent Class Statistics from Text for Robust Visual Few-Shot Learning},
author = {Yassir Bendou and Vincent Gripon and Bastien Pasdeloup and Giulia Lioi and Lukas Mauch and Fabien Cardinaux and Ghouthi Boukli Hacene},
journal= {arXiv preprint arXiv:2311.14544},
year = {2023}
}
Comments
R0-FoMo: Workshop on Robustness of Few-shot and Zero-shot Learning in Foundation Models at NeurIPS 2023