LinPart: Unsupervised Part Discovery with a Single Linear Head

Unsupervised part discovery segments objects into parts and assigns consistent identities across images without part annotations. Recent methods build on self-supervised features by adding trainable modules or partially fine-tuning the backbone. Two analyses of frozen DINOv2 features motivate a simpler design. First, nearest-centroid clustering already recovers part structure using a linear assignment rule. Second, cross-image feature neighbours agree on part identity more often than randomly reconnected pairs. Based on these observations, LinPart trains only a shared linear head on the frozen features. Its objective combines feature reconstruction and regularization to guide foreground selection, division into parts, and consistent part assignments across images. With at most 39,270 trainable parameters, LinPart outperforms prior methods. We also derive a merge score by relating the expected correspondence loss to graph modularity and incorporating changes in reconstruction and warp consistency. Applied to a trained head's predictions, the score produces partitions with fewer parts without annotations or retraining. In multiple evaluated settings, these partitions achieve higher mean agreement with annotated parts than direct training at the target part count.