  Few-shot class-incremental learning (FSCIL) aims to continually learn new
classes using a few samples while not forgetting the old classes. The key of
this task is effective knowledge transfer from the base session to the
incremental sessions. Despite the advance of existing FSCIL methods, the
proposed knowledge transfer learning schemes are sub-optimal due to the
insufficient optimization for the model's plasticity. To address this issue, we
propose a Random Episode Sampling and Augmentation (RESA) strategy that relies
on diverse pseudo incremental tasks as agents to achieve the knowledge
transfer. Concretely, RESA mimics the real incremental setting and constructs
pseudo incremental tasks globally and locally, where the global pseudo
incremental tasks are designed to coincide with the learning objective of FSCIL
and the local pseudo incremental tasks are designed to improve the model's
plasticity, respectively. Furthermore, to make convincing incremental
predictions, we introduce a complementary model with a squared
Euclidean-distance classifier as the auxiliary module, which couples with the
widely used cosine classifier to form our whole architecture. By such a way,
equipped with model decoupling strategy, we can maintain the model's stability
while enhancing the model's plasticity. Extensive quantitative and qualitative
experiments on three popular FSCIL benchmark datasets demonstrate that our
proposed method, named Knowledge Transfer-driven Relation Complementation
Network (KT-RCNet), outperforms almost all prior methods. More precisely, the
average accuracy of our proposed KT-RCNet outperforms the second-best method by
a margin of 5.26%, 3.49%, and 2.25% on miniImageNet, CIFAR100, and CUB200,
respectively. Our code is available at
https://github.com/YeZiLaiXi/KT-RCNet.git.
