  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.
