Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning
This addresses the challenge of incremental learning with limited data for AI systems that need to adapt to new classes over time, representing a strong specific gain rather than a foundational breakthrough.
The paper tackles the problem of few-shot class-incremental learning, where models must recognize new classes with few samples while retaining knowledge of old classes, and achieves state-of-the-art performance with improvements of 13%, 17%, and 11% on three benchmark datasets.
Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes. It is a challenging task since representation optimization and prototype reorganization can only be achieved under little supervision. To address this problem, we propose a novel incremental prototype learning scheme. Our scheme consists of a random episode selection strategy that adapts the feature representation to various generated incremental episodes to enhance the corresponding extensibility, and a self-promoted prototype refinement mechanism which strengthens the expression ability of the new classes by explicitly considering the dependencies among different classes. Particularly, a dynamic relation projection module is proposed to calculate the relation matrix in a shared embedding space and leverage it as the factor for bootstrapping the update of prototypes. Extensive experiments on three benchmark datasets demonstrate the above-par incremental performance, outperforming state-of-the-art methods by a margin of 13%, 17% and 11%, respectively.