Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention 事件

Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention arXiv:2606.04364v1 Announce Type: new Abstract: 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 tha