Advancing Few-Shot Class-Incremental Learning with Virtual Prototype Guidance Prompting

Few-Shot Class-Incremental Learning (FSCIL) aims to incrementally learn new class knowledge from limited samples while preserving previously knowledge from encountered classes. However, existing FSCIL methods encounter two primary challenges: (1) inadequate adaptation, where overfitting to new classes compromises the model’s adaptability, and (2) catastrophic forgetting, where previously learned knowledge is not well preserved. In this paper, we propose the Virtual Prototype Guidance Prompting (VPGP) paradigm, integrating the Multi-Grained Prompt (MGP) and Virtual-Prototype Guidance (VPG) strategies. Specifically, MGP enhances adaptation and prevents overfitting by introducing domain-general and fine-grained prompts, expanding the embedding space to capture core feature representations of novel classes. Meanwhile, VPG mitigates catastrophic forgetting by employing a dynamic fusion strategy to retrieve robust old class knowledge and generate virtual prototype, guiding the model to maintain learned knowledge across different sessions. Extensive experiments on multiple benchmark datasets demonstrate the superiority of our proposed VPGP framework.