  As a challenging problem, few-shot class-incremental learning (FSCIL)
continually learns a sequence of tasks, confronting the dilemma between slow
forgetting of old knowledge and fast adaptation to new knowledge. In this
paper, we concentrate on this "slow vs. fast" (SvF) dilemma to determine which
knowledge components to be updated in a slow fashion or a fast fashion, and
thereby balance old-knowledge preservation and new-knowledge adaptation. We
propose a multi-grained SvF learning strategy to cope with the SvF dilemma from
two different grains: intra-space (within the same feature space) and
inter-space (between two different feature spaces). The proposed strategy
designs a novel frequency-aware regularization to boost the intra-space SvF
capability, and meanwhile develops a new feature space composition operation to
enhance the inter-space SvF learning performance. With the multi-grained SvF
learning strategy, our method outperforms the state-of-the-art approaches by a
large margin.
