Proposes LDCBM, a lightweight extension of concept bottleneck models that disentangles features into concepts without region annotations to mitigate background bias and improve accuracy and interpretability.
Lan- guage in a Bottle: Language Model Guided Concept Bottlenecks for Interpretable Image Classification,
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Mitigating Spurious Background Bias in Multimedia Recognition with Disentangled Concept Bottlenecks
Proposes LDCBM, a lightweight extension of concept bottleneck models that disentangles features into concepts without region annotations to mitigate background bias and improve accuracy and interpretability.