Few-shot class-incremental learning (FSCIL) is proposed to continually learn from novel classes with only a few samples after the (pre-)training on base classes with sufficient data. However, this remains a challenge. In contrast, humans can easily recognize novel classes with a few samples. Cognitive science demonstrates that an important component of such human capability is compositional learning. This involves identifying visual primitives from learned knowledge and then composing new concepts using these transferred primitives, making incremental learning both effective and interpretable. To imitate human compositional learning, we propose a cognitive-inspired method for the FSCIL task. We define and build a compositional model based on set similarities, and then equip it with a primitive composition module and a primitive reuse module. In the primitive composition module, we propose to utilize the Centered Kernel Alignment (CKA) similarity to approximate the similarity between primitive sets, allowing the training and evaluation based on primitive compositions. In the primitive reuse module, we enhance primitive reusability by classifying inputs based on primitives replaced with the closest primitives from other classes. Experiments on three datasets validate our method, showing it outperforms current state-of-the-art methods with improved interpretability. Our code is available at https://github.com/Zoilsen/Comp-FSCIL.

翻译：少样本类增量学习（FSCIL）旨在对具有充足数据的基础类进行（预）训练后，仅利用少量样本持续学习新类。然而，这仍是一个挑战。相比之下，人类能够轻松通过少量样本识别新类。认知科学表明，这种人类能力的一个重要组成部分是组合式学习。这包括从已学知识中识别视觉基元，然后利用这些迁移的基元组合新概念，从而使增量学习既高效又可解释。为模拟人类的组合式学习，我们提出一种受认知启发的FSCIL方法。我们基于集合相似度定义并构建了一个组合模型，随后为其配备了基元组合模块和基元复用模块。在基元组合模块中，我们提出利用中心核对齐（CKA）相似度来近似基元集合间的相似性，从而支持基于基元组合的训练与评估。在基元复用模块中，我们通过使用其他类别中最接近的基元替换原有基元来对输入进行分类，从而增强基元的可复用性。在三个数据集上的实验验证了我们的方法，结果表明其以更好的可解释性超越了当前最先进的方法。我们的代码公开于 https://github.com/Zoilsen/Comp-FSCIL。