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

• URL: http://arxiv.org/abs/2107.08918v1
• Date: Mon, 19 Jul 2021 14:31:33 GMT
• Title: Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning
• Authors: Kai Zhu, Yang Cao, Wei Zhai, Jie Cheng, Zheng-Jun Zha
• Abstract summary: Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes. We propose a novel incremental prototype learning scheme that adapts the feature representation to various generated incremental episodes. 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.
• Score: 81.10531943939365
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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