Exemplar-Free Few-Shot Class-Incremental Learning with Frozen Statistics

Large-scale pretrained visual encoders have substantially raised the representation ceiling for few-shot class-incremental learning (FSCIL) on images and point clouds, allowing new classes to be learned from a few samples while old ones are retained. Yet many existing methods still replay raw exemplars, raising privacy concerns, or add prompts, auxiliary modules, or per-session gradient updates that increase complexity and overwrite previously learned knowledge. We show that exemplar-free FSCIL needs none of these: once the pretrained encoder is frozen, compact class statistics suffice. We propose FROzen STatistics (FROST), which replaces exemplar replay with two compact, complementary memories updated analytically in each session. A correlation memory accumulates sufficient statistics over features for a closed-form ridge solution, which is discriminative and needs no backpropagation but is biased toward data-rich base classes. A prototype memory keeps one normalised mean per class, so every class counts equally. Fusing the two combines discrimination with class balance and restores parity between base and novel classes. Across four point-cloud and three image benchmarks, FROST matches its exemplar-based variant and outperforms prior methods, most notably by +14.7 points on CO3D and +14.9 points on FGVC-Aircraft in terms of last-session accuracy. These results indicate that a dual-memory design over frozen features suffices for competitive exemplar-free FSCIL.