Learning to Curate What You Generate for Generalizable Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) aims to learn novel classes from limited annotations while preserving prior knowledge. Existing methods typically assume a sufficiently large base session, but this assumption fails when both base and incremental data are scarce, leading to weak initial representations, semantic drift, and unstable boundaries. We study this underexplored yet realistic setting, termed Generalizable FSCIL (G-FSCIL), where the base session itself contains only a few classes. Although synthetic data can alleviate supervision scarcity, naively mixing generated samples often introduces semantic noise and exacerbates old-new boundary conflicts. To address this, we prop...
| Authors | Junhui Yin, Yuchen Yang, Yilin Yin et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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