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.

提出了一种新的增量原型学习方案，其中包括自适应特征表示和自我推进的原型细化机制，通过动态关系投影模块来计算共享嵌入空间中的关系矩阵并将其作为引导原型更新的因素，经过三个基准数据集的广泛实验，表现优于现有的增量学习方法。

自我推进的原型细化算法用于少样本类别增量学习