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arXiv · 2505.24492

Object Centric Concept Bottlenecks

David Steinmann·Wolfgang Stammer·Antonia Wüst·Kristian Kersting
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Abstract

Developing high-performing, yet interpretable models remains a critical challenge in modern AI. Concept-based models (CBMs) attempt to address this by extracting human-understandable concepts from a global encoding (e.g., image encoding) and then applying a linear classifier on the resulting concept activations, enabling transparent decision-making. However, their reliance on holistic image encodings limits their expressiveness in object-centric real-world settings and thus hinders their ability to solve complex vision tasks beyond single-label classification. To tackle these challenges, we introduce Object-Centric Concept Bottlenecks (OCB), a framework that combines the strengths of CBMs and pre-trained object-centric foundation models, boosting performance and interpretability. We evaluate OCB on complex image datasets and conduct a comprehensive ablation study to analyze key components of the framework, such as strategies for aggregating object-concept encodings. The results show that OCB outperforms traditional CBMs and allows one to make interpretable decisions for complex visual tasks.

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David Steinmann, Wolfgang Stammer, Antonia Wüst, Kristian Kersting. 2025-10-07. Object Centric Concept Bottlenecks. https://arxiv.org/abs/2505.24492

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