AI RESEARCH
Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations
arXiv CS.CV
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ArXi:2605.16405v1 Announce Type: new Concept-bottleneck models (CBMs) are neural classifiers that compute predictions from high-level concepts extracted from the input. CBMs ensure stakeholders can understand the concepts -- and the predictions they entail -- by learning these from concept-level annotations, which are however seldom available. Recent CBM architectures work around this issue by obtaining annotations from Vision-Language Models (VLMs). While greatly broadening applicability, doing so can yield lower quality concepts and therefore less interpretable models.