# Multimodal CLIP Inference for Meta-Few-Shot Image Classification Source: [Multimodal CLIP Inference for Meta-Few-Shot Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T02:46:17.278Z Authors: Constance Ferragu, Philomene Chagniot, Vincent Coyette Published: 2024-03-26T17:47:54.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()