Intrinsic Concept Extraction Based on Compositional Interpretability

• URL: http://arxiv.org/abs/2603.11795v1
• Date: Thu, 12 Mar 2026 10:56:05 GMT
• Title: Intrinsic Concept Extraction Based on Compositional Interpretability
• Abstract summary: This paper introduces a new task called Compositional and Interpretable Intrinsic Concept Extraction (CI-ICE)<n>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.<n>Our method demonstrates outstanding performance in extracting compositionally interpretable intrinsic concepts from a single image.
• Score: 34.98614596462498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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