Real-world scenarios are usually accompanied by continuously appearing classes with scare labeled samples, which require the machine learning model to incrementally learn new classes and maintain the knowledge of base classes. In this Few-Shot Class-Incremental Learning (FSCIL) scenario, existing methods either introduce extra learnable components or rely on a frozen feature extractor to mitigate catastrophic forgetting and overfitting problems. However, we find a tendency for existing methods to misclassify the samples of new classes into base classes, which leads to the poor performance of new classes. In other words, the strong discriminability of base classes distracts the classification of new classes. To figure out this intriguing phenomenon, we observe that although the feature extractor is only trained on base classes, it can surprisingly represent the semantic similarity between the base and unseen new classes. Building upon these analyses, we propose a simple yet effective Training-frEE calibratioN (TEEN) strategy to enhance the discriminability of new classes by fusing the new prototypes (i.e., mean features of a class) with weighted base prototypes. In addition to standard benchmarks in FSCIL, TEEN demonstrates remarkable performance and consistent improvements over baseline methods in the few-shot learning scenario. Code is available at: https://github.com/wangkiw/TEEN

翻译：现实场景通常伴随着持续出现且标签样本稀少的新类别，这要求机器学习模型能够增量学习新类别并保持基类知识。在少样本类增量学习（FSCIL）场景中，现有方法要么引入额外可学习组件，要么依赖冻结的特征提取器来缓解灾难性遗忘和过拟合问题。然而，我们发现现有方法倾向于将新类别的样本误分类为基类，导致新类别性能不佳。换言之，基类的强判别性干扰了新类别的分类。为探究这一有趣现象，我们观察到：尽管特征提取器仅在基类上训练，但它令人惊讶地能够表征基类与未见新类之间的语义相似性。基于这些分析，我们提出了一种简单有效的免训练校准策略（TEEN），通过融合新类原型（即类别的均值特征）与加权的基类原型来增强新类别的判别性。除了在FSCIL标准基准上的表现外，TEEN在少样本学习场景中也展现出显著性能和相较于基线方法的一致性提升。代码已发布在：https://github.com/wangkiw/TEEN