ICML 2025

DCBM: Data-Efficient Visual Concept Bottleneck Models
Katharina Prasse * 1 Patrick Knab * 2 Sascha Marton 2 Christian Bartelt 2 Margret Keuper 1 3
Abstract
Concept Bottleneck Models (CBMs) enhance
the interpretability of neural networks by basing
predictions on human-understandable concepts.
However, current CBMs typically rely on con-
cept sets extracted from large language models
or extensive image corpora, limiting their effec-
tiveness in data-sparse scenarios. We propose
Data-efficient CBMs (DCBMs), which reduce the
need for large sample sizes during concept gener-
ation while preserving interpretability. DCBMs
define concepts as image regions detected by seg-
mentation or detection foundation models, allow-
ing each image to generate multiple concepts
across different granularities. Exclusively con-
taining dataset-specific concepts, DCBMs are
well suited for fine-grained classification and out-
of-distribution tasks. Attribution analysis using
Grad-CAM demonstrates that DCBMs deliver vi-
sual concepts that can be localized in test images.
By leveraging dataset-specific concepts instead
of predefined or general ones, DCBMs enhance
adaptability to new domains. The code is avail-
able at: https://github.com/KathPra/
DCBM.
1. Introduction
Deep neural networks (DNNs) achieve state-of-the-art per-
formance across various domains, yet their opaque nature
limits trust, particularly in safety-critical applications. To ad-
dress this, Explainable Artificial Intelligence (XAI) strives
to make model decisions more transparent and interpretable.
Concept Bottleneck Models (CBMs) (Koh et al., 2020) are
an inherently interpretable framework, as they base predic-
*Equal contribution
1Data and Web Science Group, Univer-
sity of Mannheim, Mannheim, Germany 2Clausthal University of
Technology, Clausthal, Germany 3Max-Planck-Institute for Infor-
matics, Saarland Informatics Campus. Correspondence to: Katha-
rina Prasse <katharina.prasse@uni-mannheim.de>, Patrick Knab
<patrick.kna...