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Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning - 2021

Self-Promoted Prototype Refinement For Few-Shot Class-Incremental Learning

Research Area:  Machine Learning


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.


Author(s) Name:  Kai Zhu, Yang Cao, Wei Zhai, Jie Cheng, Zheng-Jun Zha

Journal name:   Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),

Conferrence name:  

Publisher name:  arxiv

DOI:  10.48550/arXiv.2107.08918

Volume Information:  2021, pp. 6801-6810