On Prototype Composition for Few-Shot Class-Incremental Learning: Breaking Statistical Bias via Attentive and Expansive Calibration

Few-shot class-incremental learning (FSCIL) faces an inherent statistical imbalance: base classes are learned from abundant data, whereas each incremental class has only a few support examples. This imbalance produces two different vulnerabilities in prototype composition: base prototypes are trained without future classes, while centroids estimated from a few incremental supports may miss valid query directions. Their joint use can distort base–novel decisions in either direction. We refer to this decision-level asymmetry as statistical bias in prototype composition. To mitigate it, we propose BASIL (Base-Prototype Alignment with Support-Informed Local Expansion), an attentive and expansive prototype-calibration framework. First, class-weighted alignment constrains the displacement of base prototypes from their empirical centroids without discarding base-class discrimination. Second, real supports and surrogate tangent references expand the incremental-class reference set while the mean-prototype score is retained. BASIL requires no training during incremental learning and achieves strong results on widely used FSCIL benchmarks.