Exploring Interpretability for Visual Prompt Tuning with Hierarchical Concepts

• URL: http://arxiv.org/abs/2503.06084v1
• Date: Sat, 08 Mar 2025 06:12:50 GMT
• Title: Exploring Interpretability for Visual Prompt Tuning with Hierarchical Concepts
• Authors: Yubin Wang, Xinyang Jiang, De Cheng, Xiangqian Zhao, Zilong Wang, Dongsheng Li, Cairong Zhao,
• Abstract summary: We propose the first framework, named Interpretable Visual Prompt Tuning, to explore interpretability for visual prompts.<n>Visual prompts are linked to human-understandable semantic concepts, represented as a set of category-agnostic prototypes.<n>IVPT aggregates features from these regions to generate interpretable prompts, which are structured hierarchically to explain visual prompts at different granularities.
• Score: 39.92376420375139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• SDVPT: Semantic-Driven Visual Prompt Tuning for Open-World Object Counting [70.49268117587562]
  We propose a plug-and-play Semantic-Driven Visual Prompt Tuning framework (SDVPT) that transfers knowledge from the training set to unseen categories. During inference, we dynamically synthesize the visual prompts for unseen categories based on the semantic correlation between unseen and training categories.
  arXiv  Detail & Related papers  (2025-04-24T09:31:08Z)
• Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning [58.73625654718187]
  Generalized zero-shot learning aims to recognize both seen and unseen classes with the help of semantic information that is shared among different classes. Existing approaches fine-tune the visual backbone by seen-class data to obtain semantic-related visual features. This paper proposes a novel visual and semantic prompt collaboration framework, which utilizes prompt tuning techniques for efficient feature adaptation.
  arXiv  Detail & Related papers  (2025-03-29T10:17:57Z)
• Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning [114.59476118365266]
  We propose AENet, which endows semantic information into the visual prompt to distill semantic-enhanced prompt for visual representation enrichment.<n> AENet comprises two key steps: 1) exploring the concept-harmonized tokens for the visual and attribute modalities, grounded on the modal-sharing token that represents consistent visual-semantic concepts; and 2) yielding semantic-enhanced prompt via the visual residual refinement unit with attribute consistency supervision.
  arXiv  Detail & Related papers  (2024-06-05T07:59:48Z)
• LLM-based Hierarchical Concept Decomposition for Interpretable Fine-Grained Image Classification [5.8754760054410955]
  We introduce textttHi-CoDecomposition, a novel framework designed to enhance model interpretability through structured concept analysis. Our approach not only aligns with the performance of state-of-the-art models but also advances transparency by providing clear insights into the decision-making process.
  arXiv  Detail & Related papers  (2024-05-29T00:36:56Z)
• Concept-Guided Prompt Learning for Generalization in Vision-Language Models [33.361744437967126]
  We propose Concept-Guided Prompt Learning for vision-language models. We leverage the well-learned knowledge of Contrastive Language-Image Pretraining to create a visual concept cache. In order to refine the text features, we develop a projector that transforms multi-level visual features into text features.
  arXiv  Detail & Related papers  (2024-01-15T04:04:47Z)
• Tuning Multi-mode Token-level Prompt Alignment across Modalities [48.39511580746271]
  We propose a multi-mode token-level tuning framework to learn and align a set of prompt tokens across modalities. Specifically, we rely on two essential factors: 1) multi-mode prompts discovery, which guarantees diverse semantic representations, and 2) token-level alignment, which helps explore fine-grained similarity. Experiments on popular image recognition benchmarks show the superior generalization and few-shot abilities of our approach.
  arXiv  Detail & Related papers  (2023-09-25T03:20:09Z)
• Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models [64.24227572048075]
  We propose a Knowledge-Aware Prompt Tuning (KAPT) framework for vision-language models. Our approach takes inspiration from human intelligence in which external knowledge is usually incorporated into recognizing novel categories of objects.
  arXiv  Detail & Related papers  (2023-08-22T04:24:45Z)
• Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models [48.77653835765705]
  We introduce a probabilistic resolution to prompt tuning, where the label-specific prompts are generated hierarchically by first sampling a latent vector from an underlying distribution and then employing a lightweight generative model. We evaluate the effectiveness of our approach on four tasks: few-shot image recognition, base-to-new generalization, dataset transfer learning, and domain shifts.
  arXiv  Detail & Related papers  (2023-03-16T06:09:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.