Spatially Grounded Concept Bottleneck Models via Part-Factorized Attention
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, though, 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, and the model reaches the right answer for the wrong reason. We propose a part-factorized CBM (PF-CBM) that removes this freedom by construction. A frozen DINOv3 vision transformer feeds a set of part queries, each tied by name to a specific anatomical region through a fixed concept-to-part map, while whole-object attributes such as size and shape are handled separa...
| Authors | Dhanesh Ramachandram |
|---|---|
| Primary category | cs.CV |
| Published | 3 June 2026 |
| Last updated at source | 28 July 2026 |
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