  Few-shot class-incremental learning (FSCIL) aims to design machine learning
algorithms that can continually learn new concepts from a few data points,
without forgetting knowledge of old classes. The difficulty lies in that
limited data from new classes not only lead to significant overfitting issues
but also exacerbate the notorious catastrophic forgetting problems. Moreover,
as training data come in sequence in FSCIL, the learned classifier can only
provide discriminative information in individual sessions, while FSCIL requires
all classes to be involved for evaluation. In this paper, we address the FSCIL
problem from two aspects. First, we adopt a simple but effective decoupled
learning strategy of representations and classifiers that only the classifiers
are updated in each incremental session, which avoids knowledge forgetting in
the representations. By doing so, we demonstrate that a pre-trained backbone
plus a non-parametric class mean classifier can beat state-of-the-art methods.
Second, to make the classifiers learned on individual sessions applicable to
all classes, we propose a Continually Evolved Classifier (CEC) that employs a
graph model to propagate context information between classifiers for
adaptation. To enable the learning of CEC, we design a pseudo incremental
learning paradigm that episodically constructs a pseudo incremental learning
task to optimize the graph parameters by sampling data from the base dataset.
Experiments on three popular benchmark datasets, including CIFAR100,
miniImageNet, and Caltech-USCD Birds-200-2011 (CUB200), show that our method
significantly outperforms the baselines and sets new state-of-the-art results
with remarkable advantages.
