Granular Concept Circuits: Toward a Fine-Grained Circuit Discovery for Concept Representations

Dahee Kwon, Sehyun Lee, Jaesik Choi; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 2356-2365

Deep vision models have achieved remarkable classification performance by leveraging a hierarchical architecture in which human-interpretable concepts emerge through the composition of individual neurons across layers. Given the distributed nature of representations, pinpointing where specific visual concepts are encoded within a model remains a crucial yet challenging task. In this paper, we introduce an effective circuit discovery method, called Granular Concept Circuit (GCC)(Code is available at https://github.com/daheekwon/GCC)., in which each circuit represents a concept relevant to a given query. To construct each circuit, our method iteratively assesses inter-neuron connectivity, focusing on both functional dependencies and semantic alignment. By automatically discovering multiple circuits, each capturing specific concepts within that query, our approach offers a profound, concept-wise interpretation of models and is the first to identify circuits tied to specific visual concepts at a fine-grained level. We validate the versatility and effectiveness of GCCs across various deep image classification models.