Few-shot class-incremental learning (FSCIL) aims at recognizing novel classes continually with limited novel class samples. A mainstream baseline for FSCIL is first to train the whole model in the base session, then freeze the feature extractor in the incremental sessions. Despite achieving high overall accuracy, most methods exhibit notably low accuracy for incremental classes. Some recent methods somewhat alleviate the accuracy imbalance between base and incremental classes by fine-tuning the feature extractor in the incremental sessions, but they further cause the accuracy imbalance between past and current incremental classes. In this paper, we study the causes of such classification accuracy imbalance for FSCIL, and abstract them into a unified model bias problem. Based on the analyses, we propose a novel method to mitigate model bias of the FSCIL problem during training and inference processes, which includes mapping ability stimulation, separately dual-feature classification, and self-optimizing classifiers. Extensive experiments on three widely-used FSCIL benchmark datasets show that our method significantly mitigates the model bias problem and achieves state-of-the-art performance.

翻译：小样本类别增量学习（FSCIL）旨在利用有限的新类别样本持续识别新类别。FSCIL的主流基线方法通常先在基类阶段训练整个模型，随后在增量阶段冻结特征提取器。尽管整体准确率较高，但多数方法对增量类别的识别准确率显著偏低。近期部分方法通过在增量阶段微调特征提取器来缓解基类与增量类别间的准确率失衡，但这又导致了历史增量类别与当前增量类别之间的准确率失衡。本文系统研究了FSCIL中分类准确率失衡的成因，并将其抽象为统一的模型偏置问题。基于上述分析，我们提出了一种新颖方法，在训练和推理过程中缓解FSCIL问题的模型偏置，包括映射能力激发、分离式双特征分类及自优化分类器。在三个广泛使用的FSCIL基准数据集上的大量实验表明，我们的方法显著缓解了模型偏置问题，并实现了最先进的性能。