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:
this https URL

在 Few-Shot Class-Incremental Learning (FSCIL) 场景中，我们发现现有方法倾向于将新类别的样本错误地分类为基类别，导致新类别的性能表现较差。为了解决这一问题，我们提出了一种简单而有效的训练无关的校准策略（TEEN），通过将新类别的原型与加权基类别的原型相融合，以增强新类别的可区分性。在 FSCIL 标准基准测试中，TEEN 显示出出色的性能并在 few-shot learning 场景中持续改进。