Intrinsic Concept Extraction Based on Compositional Interpretability

arXiv cs.CV / 3/13/2026

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Key Points

The paper introduces CI-ICE, a new task that uses diffusion-based text-to-image models to extract composable object-level and attribute-level concepts from a single image, enabling reconstruction of the original concept through concept combinations.
HyperExpress is proposed to tackle CI-ICE with two core components: (1) a hyperbolic-space concept learning approach to achieve disentanglement while preserving hierarchical relationships, and (2) a concept-wise optimization method that maps embeddings to maintain complex inter-concept relationships and composability.
The method demonstrates outstanding performance in extracting compositionally interpretable intrinsic concepts from a single image, advancing unsupervised concept extraction and interpretability in vision-language models.
The work has potential implications for improved explainability and controllable image generation, influencing how downstream AI systems reason about object and attribute concepts.

Abstract

Unsupervised Concept Extraction aims to extract concepts from a single image; however, existing methods suffer from the inability to extract composable intrinsic concepts. To address this, this paper introduces a new task called Compositional and Interpretable Intrinsic Concept Extraction (CI-ICE). The CI-ICE task aims to leverage diffusion-based text-to-image models to extract composable object-level and attribute-level concepts from a single image, such that the original concept can be reconstructed through the combination of these concepts. To achieve this goal, we propose a method called HyperExpress, which addresses the CI-ICE task through two core aspects. Specifically, first, we propose a concept learning approach that leverages the inherent hierarchical modeling capability of hyperbolic space to achieve accurate concept disentanglement while preserving the hierarchical structure and relational dependencies among concepts; second, we introduce a concept-wise optimization method that maps the concept embedding space to maintain complex inter-concept relationships while ensuring concept composability. Our method demonstrates outstanding performance in extracting compositionally interpretable intrinsic concepts from a single image.