  Foundational vision-language models such as CLIP are becoming a new paradigm
in vision, due to their excellent generalization abilities. However, adapting
these models for downstream tasks while maintaining their generalization
remains a challenge. In literature, one branch of methods adapts CLIP by
learning prompts using visual information. While effective, most of these works
require labeled data which is not practical, and often struggle to generalize
towards new datasets due to over-fitting on the source data. An alternative
approach resorts to training-free methods by generating class descriptions from
large language models (LLMs) and perform prompt ensembling. However, these
methods often generate class specific prompts that cannot be transferred to
other classes, which incur higher costs by generating LLM descriptions for each
class separately. In this work, we propose to combine the strengths of these
both streams of methods by learning prompts using only text data derived from
LLMs. As supervised training of prompts is not trivial due to absence of
images, we develop a training approach that allows prompts to extract rich
contextual knowledge from LLM data. Moreover, with LLM contextual data mapped
within the learned prompts, it enables zero-shot transfer of prompts to new
classes and datasets potentially cutting the LLM prompt engineering cost. To
the best of our knowledge, this is the first work that learns generalized
prompts using text only data. We perform extensive evaluations on 4 benchmarks
where our method improves over prior ensembling works while being competitive
to those utilizing labeled images. Our code and pre-trained models are
available at https://github.com/muzairkhattak/ProText.
