A Few-Shot Class Incremental Learning Method Using Graph Neural Networks

Few-shot class incremental learning (FSCIL) aims to continuously learn new classes from limited training samples while retaining previously acquired knowledge. Existing approaches are not fully capable of balancing stability and plasticity in dynamic scenarios. To overcome this limitation, we introduce a novel FSCIL framework that leverages graph neural networks (GNNs) to model interdependencies between different categories and enhance cross-modal alignment. Our framework incorporates three key components: 1) a Graph Isomorphism Network (GIN) to propagate contextual relationships among prompts; 2) a Hamiltonian Graph Network with Energy Conservation (HGN-EC) to stabilize training dynamics via energy conservation constraints; and 3) an Adversarially Constrained Graph Autoencoder (ACGA) to enforce latent space consistency. By integrating these components with a parameter-efficient CLIP backbone, our method dynamically adapts graph structures to model semantic correlations between textual and visual modalities. Additionally, contrastive learning with energy-based regularization is employed to mitigate catastrophic forgetting and improve generalization. Comprehensive experiments on benchmark datasets validate the framework's incremental accuracy and stability compared to state-of-the-art baselines. This work advances FSCIL by unifying graph-based relational reasoning with physics-inspired optimization, offering a scalable and interpretable framework. Code is available at: https://github.com/aries-yqian/ACHG-CLIP.