Object Centric Concept Bottlenecks

• URL: http://arxiv.org/abs/2505.24492v2
• Date: Wed, 04 Jun 2025 06:54:15 GMT
• Title: Object Centric Concept Bottlenecks
• Authors: David Steinmann, Wolfgang Stammer, Antonia Wüst, Kristian Kersting,
• Abstract summary: We introduce Object-Centric Concept Bottlenecks (OCB), a framework that combines the strengths of CBMs and pre-trained object-centric foundation models.<n>We evaluate OCB on complex image datasets and conduct a comprehensive ablation study to analyze key components of the framework.<n>The results show that OCB outperforms traditional CBMs and allows one to make interpretable decisions for complex visual tasks.
• Score: 22.074896812195437
• License: http://creativecommons.org/licenses/by/4.0/
• 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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