  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.
