Alleviating Regional Shortcuts for Few-Shot Class-Incremental Learning
Few-shot class-incremental learning (FSCIL) aims to incrementally learn novel classes with only a few samples while avoiding forgetting base classes. However, current methods show a tendency to misclassify novel-class samples into base classes, which we find to be caused by the excessive focus on base-class-discriminative regions on novel-class samples. In this work, we aim to explore the underlying mechanism for an interpretation and solution. We first provide a compositional view to analyze the transferred and reused spatial patterns on novel-class samples. Then, through extensive experiments and theoretical analysis, we identify both empirically and theoretically that a shortcut exists...
| Authors | Haichen Zhou, Yazhe Lyu, Yixiong Zou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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