Exploring Interpretability for Visual Prompt Tuning with Hierarchical Concepts

2025-03-08

AI Summary

Visual Prompt Tuning (VPT) suffers from poor interpretability, hindering its trustworthy deployment and semantic knowledge discovery. To address this, we propose IVPT—the first interpretable VPT framework—centered on category-agnostic, hierarchical concept prototypes (e.g., texture → part → structure) that explicitly align visual prompts with multi-granularity semantic spaces. IVPT enables end-to-end, region-aware, and semantically disentangled prompt generation. Its methodology comprises hierarchical prototype learning, region-wise feature aggregation, semantics-driven prompt generation, and quantitative interpretability evaluation. On fine-grained classification benchmarks, IVPT significantly outperforms standard VPT and existing interpretable methods, while delivering verifiable, human-understandable, cross-granularity semantic explanations. Crucially, it achieves this without compromising accuracy, jointly optimizing task performance and explanation fidelity.

Technology Category

• Computer Vision: Interpretability, Explainability, and Transparency
• Machine Learning: Transparent, Interpretable, Explainable ML
• Natural Language Processing: Prompt Engineering / Prompting

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

Visual prompt tuning offers significant advantages for adapting pre-trained visual foundation models to specific tasks. However, current research provides limited insight into the interpretability of this approach, which is essential for enhancing AI reliability and enabling AI-driven knowledge discovery. In this paper, rather than learning abstract prompt embeddings, we propose the first framework, named Interpretable Visual Prompt Tuning (IVPT), to explore interpretability for visual prompts, by introducing hierarchical concept prototypes. Specifically, visual prompts are linked to human-understandable semantic concepts, represented as a set of category-agnostic prototypes, each corresponding to a specific region of the image. Then, IVPT aggregates features from these regions to generate interpretable prompts, which are structured hierarchically to explain visual prompts at different granularities. Comprehensive qualitative and quantitative evaluations on fine-grained classification benchmarks show its superior interpretability and performance over conventional visual prompt tuning methods and existing interpretable methods.

Problem

• Enhancing interpretability of visual prompt tuning
• Linking visual prompts to human-understandable semantic concepts
• Improving AI reliability and knowledge discovery through interpretable prompts

Innovation

• Introduces hierarchical concept prototypes for interpretability
• Links visual prompts to human-understandable semantic concepts
• Aggregates features hierarchically for granular prompt explanation