  Going beyond mere fine-tuning of vision-language models (VLMs), learnable
prompt tuning has emerged as a promising, resource-efficient alternative.
Despite their potential, effectively learning prompts faces the following
challenges: (i) training in a low-shot scenario results in overfitting,
limiting adaptability, and yielding weaker performance on newer classes or
datasets; (ii) prompt-tuning's efficacy heavily relies on the label space, with
decreased performance in large class spaces, signaling potential gaps in
bridging image and class concepts. In this work, we investigate whether better
text semantics can help address these concerns. In particular, we introduce a
prompt-tuning method that leverages class descriptions obtained from Large
Language Models (LLMs). These class descriptions are used to bridge image and
text modalities. Our approach constructs part-level description-guided image
and text features, which are subsequently aligned to learn more generalizable
prompts. Our comprehensive experiments conducted across 11 benchmark datasets
show that our method outperforms established methods, demonstrating substantial
improvements.
