SAAS: Size-Aware Anchor Selection for Generalized Category Discovery

Generalized category discovery (GCD) assigns unlabeled images to known and novel categories using supervision from known classes. Reliable groups of similar images can help correct category predictions, but assessing their reliability through graph density can penalize larger groups. This bias arises because sparse nearest-neighbor graphs limit the connections available to each image, whereas complete-graph normalization counts all possible connections within a group. We propose Size-Aware Anchor Selection (SAAS), a general reliability assessment strategy for selecting reliable local groups under sparse graph constraints, instantiated as a post-hoc prediction correction framework without additional training. SAAS normalizes the observed connections by the maximum number permitted by the group size and neighborhood size. It selects groups whose images are similar and whose membership remains consistent across two augmented views, using these groups as anchors for correction. Similarity to the selected groups guides corrections to ambiguous predictions. We evaluate SAAS on six generic and fine-grained GCD benchmarks using DINOv2 pretraining. On CUB, the resulting pipeline achieves 88.67% overall accuracy, with 85.80% on known classes and 90.10% on novel classes. Ablation studies show that removing the group-size cap alone leaves predictions unchanged, whereas the proposed normalization improves accuracy and enables further gains when the cap is removed. These results highlight the importance of accounting for sparse connectivity when selecting reliable groups for GCD prediction correction.