AI RESEARCH
Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention
arXiv CS.LG
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ArXi:2606.04364v1 Announce Type: cross Concept bottleneck models (CBMs) predict a layer of human-named attributes before predicting a class, which makes their decisions auditable. On fine-grained recognition tasks the concept heads are usually free to attend anywhere in the image, so a head named for one body region can be satisfied by evidence on another. This work studies a part-factorized CBM that removes that freedom by construction. The method has three components built on a frozen DINOv3 vision transformer.