Few-shot class-incremental learning (FSCIL) aims to design machine learning algorithms that can continually learn new concepts from a few data points, without forgetting knowledge of old classes. The difficulty lies in that limited data from new classes not only lead to significant overfitting issues but also exacerbates the notorious catastrophic forgetting problems. However, existing FSCIL methods ignore the semantic relationships between sample-level and class-level. % Using the advantage that graph neural network (GNN) can mine rich information among few samples, In this paper, we designed a two-level graph network for FSCIL named Sample-level and Class-level Graph Neural Network (SCGN). Specifically, a pseudo incremental learning paradigm is designed in SCGN, which synthesizes virtual few-shot tasks as new tasks to optimize SCGN model parameters in advance. Sample-level graph network uses the relationship of a few samples to aggregate similar samples and obtains refined class-level features. Class-level graph network aims to mitigate the semantic conflict between prototype features of new classes and old classes. SCGN builds two-level graph networks to guarantee the latent semantic of each few-shot class can be effectively represented in FSCIL. Experiments on three popular benchmark datasets show that our method significantly outperforms the baselines and sets new state-of-the-art results with remarkable advantages.

翻译：少样本类增量学习旨在设计能够从少量数据中持续学习新概念的机器学习算法，同时不遗忘旧类别的知识。其难点在于新类别的有限数据不仅会导致显著的过拟合问题，还会加剧严重的灾难性遗忘问题。然而，现有少样本类增量学习方法忽略了样本级与类别级之间的语义关系。利用图神经网络能够挖掘少量样本间丰富信息的优势，本文设计了一种用于少样本类增量学习的双层图网络，命名为样本级与类别级图神经网络。具体而言，该网络设计了一种伪增量学习范式，通过合成虚拟的少样本任务作为新任务，预先优化模型参数。样本级图网络利用少量样本间的关系聚合相似样本，获得精炼的类别级特征。类别级图网络旨在缓解新类别与旧类别原型特征之间的语义冲突。该网络通过构建双层图网络，确保少样本类增量学习中每个少样本类别的潜在语义得到有效表征。在三个主流基准数据集上的实验表明，我们的方法显著优于基线方法，并以显著优势达到了新的最佳性能。