Beyond the Observed: Knowledge-Informed Controllable Augmentation for Few-Shot Class-Incremental Learning

Few-Shot Class-Incremental Learning (FSCIL) addresses the practical need to continually acquire novel classes from limited labeled data while retaining previously learned knowledge. While previous studies primarily focus on model-side strategies for improving adaptation and knowledge retention, the role of data-side augmentation remains relatively underexplored. We argue that a key limitation of few-shot scenarios lies in the severely limited intra-class variations captured by sparse observations, and that external knowledge can provide useful priors for exploring plausible variations beyond the observed samples. Based on this insight, we propose Knowledge-Informed Controllable Augmentation (KICA) that derives plausible variation instructions from external class knowledge and realizes them through image-conditioned diffusion-based editing. By combining the visual evidence of few-shot observations with external variation knowledge, KICA preserves class-specific characteristics while enriching intra-class variations, achieving broader coverage of real intra-class variations as evidenced by distributional analyses. Extensive experiments across multiple FSCIL benchmarks and network architectures demonstrate consistent performance improvements, which validate the effectiveness of knowledge-informed augmentation and motivate a datacentric direction through expanded intra-class variation coverage.