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.

翻译：作为一项具有挑战性的问题，小样本增量学习（FSCIL）需持续学习一系列任务，面临旧知识缓慢遗忘与新知识快速适应之间的两难困境。本文聚焦于这种"慢速与快速"（SvF）困境，旨在确定哪些知识成分应以缓慢方式或快速方式进行更新，从而平衡旧知识保留与新知识适应。我们提出一种多粒度SvF学习策略，从两个不同粒度应对SvF困境：空间内（同一特征空间内）与空间间（两个不同特征空间之间）。该策略设计了一种新颖的频率感知正则化方法以提升空间内SvF能力，同时开发了一种新的特征空间组合操作以增强空间间SvF学习性能。通过多粒度SvF学习策略，我们的方法以显著优势超越了现有最优方法。