Few-shot class-incremental learning (FSCIL) aims to acquire knowledge from
novel classes with limited samples while retaining information about base
classes. Existing methods address catastrophic forgetting and overfitting by
freezing the feature extractor during novel-class learning. However, these
methods usually tend to cause the confusion between base and novel classes,
i.e., classifying novel-class samples into base classes. In this paper, we
delve into this phenomenon to study its cause and solution. We first interpret
the confusion as the collision between the novel-class and the base-class
region in the feature space. Then, we find the collision is caused by the
label-irrelevant redundancies within the base-class feature and pixel space.
Through qualitative and quantitative experiments, we identify this redundancy
as the shortcut in the base-class training, which can be decoupled to alleviate
the collision. Based on this analysis, to alleviate the collision between base
and novel classes, we propose a method for FSCIL named Redundancy Decoupling
and Integration (RDI). RDI first decouples redundancies from base-class space
to shrink the intra-base-class feature space. Then, it integrates the
redundancies as a dummy class to enlarge the inter-base-class feature space.
This process effectively compresses the base-class feature space, creating
buffer space for novel classes and alleviating the model's confusion between
the base and novel classes. Extensive experiments across benchmark datasets,
including CIFAR-100, miniImageNet, and CUB-200-2011 demonstrate that our method
achieves state-of-the-art performance.

少样本类增量学习 (FSCIL) 的研究旨在从有限的样本中获取关于新类的知识，同时保留关于基类的信息。本文通过定量和定性实验，探究了新类与基类的碰撞现象，发现基类特征和像素空间中的冗余是碰撞的主要原因。基于此分析，为了减轻基类与新类之间的碰撞，我们提出了一种名为冗余解耦与整合 (RDI) 的 FSCIL 方法。该方法通过将基类空间中的冗余解耦，压缩了基类特征空间，为新类创造了缓冲空间，从而有效地缓解了模型对基类与新类之间的混淆。在包括 CIFAR-100、miniImageNet 和 CUB-200-2011 在内的基准数据集上进行的大量实验表明，我们的方法达到了最先进的性能。