  Image recognition has recently witnessed a paradigm shift, where
vision-language models are now used to perform few-shot classification based on
textual prompts. Among these, the CLIP model has shown remarkable capabilities
for zero-shot transfer by matching an image and a custom textual prompt in its
latent space. This has paved the way for several works that focus on
engineering or learning textual contexts for maximizing CLIP's classification
capabilities. In this paper, we follow this trend by learning an ensemble of
prompts for image classification. We show that learning diverse and possibly
shorter contexts improves considerably and consistently the results rather than
relying on a single trainable prompt. In particular, we report better few-shot
capabilities with no additional cost at inference time. We demonstrate the
capabilities of our approach on 11 different benchmarks.
