  Few-shot class-incremental learning (FSCIL) is designed to incrementally
recognize novel classes with only few training samples after the (pre-)training
on base classes with sufficient samples, which focuses on both base-class
performance and novel-class generalization. A well known modification to the
base-class training is to apply a margin to the base-class classification.
However, a dilemma exists that we can hardly achieve both good base-class
performance and novel-class generalization simultaneously by applying the
margin during the base-class training, which is still under explored. In this
paper, we study the cause of such dilemma for FSCIL. We first interpret this
dilemma as a class-level overfitting (CO) problem from the aspect of pattern
learning, and then find its cause lies in the easily-satisfied constraint of
learning margin-based patterns. Based on the analysis, we propose a novel
margin-based FSCIL method to mitigate the CO problem by providing the pattern
learning process with extra constraint from the margin-based patterns
themselves. Extensive experiments on CIFAR100, Caltech-USCD Birds-200-2011
(CUB200), and miniImageNet demonstrate that the proposed method effectively
mitigates the CO problem and achieves state-of-the-art performance.
