  Prompt learning has become a prevalent strategy for adapting vision-language
foundation models to downstream tasks. As large language models (LLMs) have
emerged, recent studies have explored the use of category-related descriptions
as input to enhance prompt effectiveness. Nevertheless, conventional
descriptions fall short of structured information that effectively represents
the interconnections among entities or attributes linked to a particular
category. To address this limitation and prioritize harnessing structured
knowledge, this paper advocates for leveraging LLMs to build a graph for each
description to model the entities and attributes describing the category, as
well as their correlations. Preexisting prompt tuning methods exhibit
inadequacies in managing this structured knowledge. Consequently, we propose a
novel approach called Hierarchical Prompt Tuning (HPT), which enables
simultaneous modeling of both structured and conventional linguistic knowledge.
Specifically, we introduce a relationship-guided attention module to capture
pair-wise associations among entities and attributes for low-level prompt
learning. In addition, by incorporating high-level and global-level prompts
modeling overall semantics, the proposed hierarchical structure forges
cross-level interlinks and empowers the model to handle more complex and
long-term relationships. Extensive experiments demonstrate that our HPT shows
strong effectiveness and generalizes much better than existing SOTA methods.
Our code is available at https://github.com/Vill-Lab/2024-AAAI-HPT.
