Coarse-to-Fine Concept Bottleneck Models

Konstantinos P. Panousis, Dino Ienco, Diego Marcos

Keywords: Interpretability, Explainability, Concept Bottleneck Models, Sparsity, Multimodal Models, Concepts, Textual Descriptions, Bayesian, Masking

TL;DR: We propose a novel coarse-to-fine construction for concept discovery; we do not solely rely on the similarity between concepts and the whole image, but we also consider granular information residing in patch-specific regions of the image.

Abstract: Deep learning algorithms have recently gained significant attention due to their impressive performance. However, their high complexity and un-interpretable mode of operation hinders their confident deployment in real-world safety-critical tasks. This work targets ante hoc interpretability, and specifically Concept Bottleneck Models (CBMs). Our goal is to design a framework that admits a highly interpretable decision making process with respect to human understandable concepts, on two levels of granularity. To this end, we propose a novel two-level concept discovery formulation leveraging: (i) recent advances in vision-language models, and (ii) an innovative formulation for coarse-to-fine concept selection via data-driven and sparsity inducing Bayesian arguments. Within this framework, concept information does not solely rely on the similarity between the whole image and general unstructured concepts; instead, we introduce the notion of concept hierarchy to uncover and exploit more granular concept information residing in patch-specific regions of the image scene. As we experimentally show, the proposed construction not only outperforms recent CBM approaches, but also yields a principled framework towards interpetability.

Primary Area: Interpretability and explainability

Submission Number: 9247