Few-Shot Class-Incremental Learning with Prompt Alignment and Subspace Prototype Aggregation

Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes with only a few samples, making it more challenging than traditional Class-Incremental Learning (CIL) due to the scarcity of available samples. The imbalance in sample distribution further complicates balancing the abundant base data with the scarce incremental data. While the model must fully leverage the extensive base data to guide the learning of subsequent tasks, it must also avoid over-relying on these data, as doing so could degrade its generalization capability and impede the learning of new incremental tasks. To address these challenges, we propose a novel framework for few-shot incremental learning, incorporating tailored prompt alignment strategies for both the base and incremental session. In the base session, we strike a balance between task-specific and task-agnostic knowledge to preserve the model’s generalization ability. In the incremental session, we mitigate the overfitting issue typically associated with few-shot learning. Furthermore, to tackle the prototype network bias caused by the imbalance in sample distribution, we propose a subspace prototype aggregation module, which effectively alleviates prediction bias in the incremental phase. Extensive experiments conducted on three benchmark datasets—CIFAR-100, miniImageNet, and CUB-200—demonstrate that our approach achieves state-of-the-art (SOTA) performance in FSCIL.