Yi-Lin Yin

Learning to Curate What You Generate for Generalizable Few-Shot Class-Incremental Learning

This work proposes a framework that curates trustworthy synthetic knowledge for stable G-FSCIL, and designs a boundary-stable incremental adaptation scheme, including synthetic-informed prototype initialization and bidirectional boundary calibration to mitigate old-new conflicts.