  In recent literature, few-shot classification has predominantly been defined
by the N-way k-shot meta-learning problem. Models designed for this purpose are
usually trained to excel on standard benchmarks following a restricted setup,
excluding the use of external data. Given the recent advancements in large
language and vision models, a question naturally arises: can these models
directly perform well on meta-few-shot learning benchmarks? Multimodal
foundation models like CLIP, which learn a joint (image, text) embedding, are
of particular interest. Indeed, multimodal training has proven to enhance model
robustness, especially regarding ambiguities, a limitation frequently observed
in the few-shot setup. This study demonstrates that combining modalities from
CLIP's text and image encoders outperforms state-of-the-art meta-few-shot
learners on widely adopted benchmarks, all without additional training. Our
results confirm the potential and robustness of multimodal foundation models
like CLIP and serve as a baseline for existing and future approaches leveraging
such models.
