Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models

• URL: http://arxiv.org/abs/2312.06323v1
• Date: Mon, 11 Dec 2023 12:14:06 GMT
• Title: Learning Hierarchical Prompt with Structured Linguistic Knowledge for Vision-Language Models
• Authors: Yubin Wang, Xinyang Jiang, De Cheng, Dongsheng Li, Cairong Zhao
• Abstract summary: We propose a novel approach to harnessing structured knowledge in large language models (LLMs) 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, the proposed hierarchical structure forges cross-level interlinks and empowers the model to handle more complex and long-term relationships.
• Score: 43.56153167864033
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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