Few-shot class-incremental learning (FSCIL) aims to build machine learning model that can continually learn new concepts from a few data samples, without forgetting knowledge of old classes. The challenges of FSCIL lies in the limited data of new classes, which not only lead to significant overfitting issues but also exacerbates the notorious catastrophic forgetting problems. As proved in early studies, building sample relationships is beneficial for learning from few-shot samples. In this paper, we promote the idea to the incremental scenario, and propose a Sample-to-Class (S2C) graph learning method for FSCIL. Specifically, we propose a Sample-level Graph Network (SGN) that focuses on analyzing sample relationships within a single session. This network helps aggregate similar samples, ultimately leading to the extraction of more refined class-level features. Then, we present a Class-level Graph Network (CGN) that establishes connections across class-level features of both new and old classes. This network plays a crucial role in linking the knowledge between different sessions and helps improve overall learning in the FSCIL scenario. Moreover, we design a multi-stage strategy for training S2C model, which mitigates the training challenges posed by limited data in the incremental process. The multi-stage training strategy is designed to build S2C graph from base to few-shot stages, and improve the capacity via an extra pseudo-incremental stage. Experiments on three popular benchmark datasets show that our method clearly outperforms the baselines and sets new state-of-the-art results in FSCIL.

翻译：少样本类增量学习旨在构建能够从少量数据样本中持续学习新概念、同时不遗忘旧类别知识的机器学习模型。其挑战在于新类别的数据量有限，这不仅导致严重的过拟合问题，还加剧了灾难性遗忘这一难题。早期研究表明，构建样本关系有助于从少样本中学习。本文将此思想推广至增量场景，并提出一种面向少样本类增量学习的样本到类图学习方法。具体而言，我们提出一种专注于分析单个会话内样本关系的样本级图网络。该网络有助于聚合相似样本，最终提取更精细的类级特征。随后，我们提出一种类级图网络，用于建立新老类别类级特征之间的连接。该网络在连接不同会话的知识中发挥关键作用，有助于提升少样本类增量学习场景的整体学习效果。此外，我们设计了一种多阶段策略来训练样本到类模型，以缓解增量过程中数据有限带来的训练挑战。该多阶段训练策略旨在从基础阶段到少样本阶段逐步构建样本到类图，并通过额外的伪增量阶段提升模型能力。在三个主流基准数据集上的实验表明，我们的方法明显优于基线方法，并在少样本类增量学习中取得了最新的最佳结果。