VH-CBM uses a Gaussian process in VLM embedding space to propagate sparse human annotations and improve concept accuracy and calibration over pure VLM-guided concept bottleneck models.
Is disentan- glement all you need? Comparing concept-based & disen- tanglement approaches.CoRR, abs/2104.06917
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A VAE and slot-attention scheme learns interpretable object concepts from 1% labels, enabling symbolic reasoning that outperforms foundation models under domain shift.
Empirical tests of VLM-CBMs show VLM supervision differs from expert annotations depending on task and that concept accuracy correlates weakly with quality metrics.
citing papers explorer
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Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations
VH-CBM uses a Gaussian process in VLM embedding space to propagate sparse human annotations and improve concept accuracy and calibration over pure VLM-guided concept bottleneck models.
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Weakly Supervised Concept Learning for Object-centric Visual Reasoning
A VAE and slot-attention scheme learns interpretable object concepts from 1% labels, enabling symbolic reasoning that outperforms foundation models under domain shift.
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If Concept Bottlenecks are the Question, are Foundation Models the Answer?
Empirical tests of VLM-CBMs show VLM supervision differs from expert annotations depending on task and that concept accuracy correlates weakly with quality metrics.