Hierarchical Concept Embedding & Pursuit for Interpretable Image Classification

ArXi:2602.11448v2 Announce Type: replace Interpretable-by-design models are gaining traction in computer vision because they provide faithful explanations for their predictions. In image classification, these models typically recover human-interpretable concepts from an image and use them for classification. Sparse concept recovery methods leverage the latent space of vision-language models to represent image embeddings as a sparse combination of concept embeddings.