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

arXiv: https://arxiv.org/abs/2312.06323 · HTML: https://mlsanity.com/paper/2312.06323/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Placed in Prompt learning and adaptation of CLIP, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 25 of its 42 references that are in the map.
- First posted on arXiv 11 Dec 2023.

## Placed in

- Multimodal LLMs / vision-language models / [Prompt learning and adaptation of CLIP](https://mlsanity.com/mllm/prompt-learning-and-adaptation-of-clip/)

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Fine-Grained Visual Classification of Aircraft](https://mlsanity.com/paper/1306.5151/) (2013)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [Learning Robust Global Representations by Penalizing Local Predictive Power](https://mlsanity.com/paper/1905.13549/) (2019)
- [Learning to Prompt for Continual Learning](https://mlsanity.com/paper/2112.08654/) (2021)
- [Florence: A New Foundation Model for Computer Vision](https://mlsanity.com/paper/2111.11432/) (2021)
- [FILIP: Fine-grained Interactive Language-Image Pre-Training](https://mlsanity.com/paper/2111.07783/) (2021)
- [DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting](https://mlsanity.com/paper/2112.01518/) (2021)
- [SLIP: Self-supervision meets Language-Image Pre-training](https://mlsanity.com/paper/2112.12750/) (2021)
- [Symbolic Discovery of Optimization Algorithms](https://mlsanity.com/paper/2302.06675/) (2023)
- [Zero-Shot Video Question Answering via Frozen Bidirectional Language Models](https://mlsanity.com/paper/2206.08155/) (2022)
- [Unified Contrastive Learning in Image-Text-Label Space](https://mlsanity.com/paper/2204.03610/) (2022)
- [VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image Captioning](https://mlsanity.com/paper/2102.10407/) (2021)
- [Combined Scaling for Zero-shot Transfer Learning](https://mlsanity.com/paper/2111.10050/) (2021)
- [Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners](https://mlsanity.com/paper/2303.02151/) (2023)
- [K-LITE: Learning Transferable Visual Models with External Knowledge](https://mlsanity.com/paper/2204.09222/) (2022)
- [Learning Customized Visual Models with Retrieval-Augmented Knowledge](https://mlsanity.com/paper/2301.07094/) (2023)
- [Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos](https://mlsanity.com/paper/2203.14104/) (2022)
- [Multi-task Neural Network for Non-discrete Attribute Prediction in Knowledge Graphs](https://mlsanity.com/paper/1708.04828/) (2017)

## Built on by (most cited first, 23 of 23)

- [Advancing Textual Prompt Learning with Anchored Attributes](https://mlsanity.com/paper/2412.09442/) (2024)
- [DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models](https://mlsanity.com/paper/2503.13443/) (2025)
- [Hierarchical Cross-modal Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2507.14976/) (2025)
- [Rethinking Misalignment in Vision-Language Model Adaptation from a Causal Perspective](https://mlsanity.com/paper/2410.12816/) (2024)
- [Tree of Attributes Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2410.11201/) (2024)
- [The LLM Bottleneck: Why Open-Source Vision LLMs Struggle with Hierarchical Visual Recognition](https://mlsanity.com/paper/2505.24840/) (2025)
- [MMRL++: Parameter-Efficient and Interaction-Aware Representation Learning for Vision-Language Models](https://mlsanity.com/paper/2505.10088/) (2025)
- [Adversarial Domain Prompt Tuning and Generation for Single Domain Generalization](https://mlsanity.com/paper/2606.21736/) (2026)
- [GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection](https://mlsanity.com/paper/2504.14919/) (2025)
- [ActPrompt: In-Domain Feature Adaptation via Action Cues for Video Temporal Grounding](https://mlsanity.com/paper/2408.06622/) (2024)
- [HPT++: Hierarchically Prompting Vision-Language Models with Multi-Granularity Knowledge Generation and Improved Structure Modeling](https://mlsanity.com/paper/2408.14812/) (2024)
- [Exploring Interpretability for Visual Prompt Tuning with Cross-layer Concepts](https://mlsanity.com/paper/2503.06084/) (2025)
- [Causality-guided Prompt Learning for Vision-language Models via Visual Granulation](https://mlsanity.com/paper/2509.03803/) (2025)
- [Craft: Cross-modal Aligned Features Improve Robustness of Prompt Tuning](https://mlsanity.com/paper/2407.15894/) (2024)
- [LPT: Less-overfitting Prompt Tuning for Vision-Language Model](https://mlsanity.com/paper/2410.10247/) (2024)
- [LLM-enhanced Action-aware Multi-modal Prompt Tuning for Image-Text Matching](https://mlsanity.com/paper/2506.23502/) (2025)
- [SemPT: Semantic Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2508.10645/) (2025)
- [FVG-PT: Adaptive Foreground View-Guided Prompt Tuning for Vision-Language Models](https://mlsanity.com/paper/2603.08708/) (2026)
- [CASA: Class-Agnostic Shared Attributes in Vision-Language Models for Efficient Incremental Object Detection](https://mlsanity.com/paper/2410.05804/) (2024)
- [InPK: Infusing Prior Knowledge into Prompt for Vision-Language Models](https://mlsanity.com/paper/2502.19777/) (2025)
- [Integrated Structural Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2507.05677/) (2025)
- [FedMGP: Personalized Federated Learning with Multi-Group Text-Visual Prompts](https://mlsanity.com/paper/2511.00480/) (2025)
- [HeBA: Heterogeneous Bottleneck Adapters for Robust Vision-Language Models](https://mlsanity.com/paper/2603.16653/) (2026)

## Abstract (arXiv)

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.