Diagnosing Prototype Estimation in Frozen-Feature Few-Shot Class-Incremental Learning: Training-Free Geometry Selection and Unlabeled-Query Prototype Rectification
A study of prototype estimation across 16 encoder–dataset pairs. ShrinkSel selects prototype geometry using base data, while Query Prototype Rectification uses unlabeled queries to refine novel-class prototypes.
Method & evaluation
- QPR added 1.21–2.03 accuracy points on CLIP ViT-B/16 across three datasets.
- Finds that noisy few-shot prototype estimates are a more significant source of error than the classifier fitted afterward.
- ShrinkSel selects prototype radius and covariance shrinkage from base-class data, with shared LDA as a fallback. It exceeded LDA on 15 of 16 pairs by 0.04–1.52 accuracy points.
- Compares more than a dozen baselines with matched features and supports. Additional experiments cover MAE and supervised ResNet encoders, as well as Aircraft and DTD domains.




