  Few-Shot Class-Incremental Learning (FSCIL) has gained considerable attention
in recent years for its pivotal role in addressing continuously arriving
classes. However, it encounters additional challenges. The scarcity of samples
in new sessions intensifies overfitting, causing incompatibility between the
output features of new and old classes, thereby escalating catastrophic
forgetting. A prevalent strategy involves mitigating catastrophic forgetting
through the Explicit Memory (EM), which comprise of class prototypes. However,
current EM-based methods retrieves memory globally by performing
Vector-to-Vector (V2V) interaction between features corresponding to the input
and prototypes stored in EM, neglecting the geometric structure of local
features. This hinders the accurate modeling of their positional relationships.
To incorporate information of local geometric structure, we extend the V2V
interaction to Graph-to-Graph (G2G) interaction. For enhancing local structures
for better G2G alignment and the prevention of local feature collapse, we
propose the Local Graph Preservation (LGP) mechanism. Additionally, to address
sample scarcity in classes from new sessions, the Contrast-Augmented G2G
(CAG2G) is introduced to promote the aggregation of same class features thus
helps few-shot learning. Extensive comparisons on CIFAR100, CUB200, and the
challenging ImageNet-R dataset demonstrate the superiority of our method over
existing methods.
