Unlocking Frozen Potential: Dynamic Analytical Prototypes for Few-Shot Class-Incremental Learning

Few-Shot Class Incremental Learning challenges models to continuously learn novel categories from very few samples while mitigating the stability-plasticity dilemma. Recent methods increasingly leverage the robust generalization of Pre-Trained Models (PTMs) and stable prototype-based classifiers. We identify a critical limitation: Rich Features but Constrained Potential. Although PTMs provide rich generic representations, their potential is constrained by the frozen backbone's insufficient adaptation to novel concepts, the statistical bias of prototypes derived from very few samples and the absence of global discriminative optimization in static prototypes. Consequently, static prototype-based methods often struggle with feature entanglement and unreliable prototypes. To unlock the frozen potential, we propose Dynamic Analytical Prototypes that refine the post-backbone discriminative space. The first module Prior-Guided Distribution Calibration effectively improves the discriminative capability of representations by leveraging the base-class priors. Second, to achieve a global optimal decision, we derive a Global Knowledge-Aware Solver. This recursive solution updates class-balanced weights and theoretically mitigates the risk of catastrophic forgetting. Extensive experiments demonstrate consistent improvements over representative feature- and classifier-level optimization methods, validating its balance between stability and plasticity.