  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.
