  To imitate the ability of keeping learning of human, continual learning which
can learn from a never-ending data stream has attracted more interests
recently. In all settings, the online class incremental learning (OCIL), where
incoming samples from data stream can be used only once, is more challenging
and can be encountered more frequently in real world. Actually, all continual
learning models face a stability-plasticity dilemma, where the stability means
the ability to preserve old knowledge while the plasticity denotes the ability
to incorporate new knowledge. Although replay-based methods have shown
exceptional promise, most of them concentrate on the strategy for updating and
retrieving memory to keep stability at the expense of plasticity. To strike a
preferable trade-off between stability and plasticity, we propose an Adaptive
Focus Shifting algorithm (AFS), which dynamically adjusts focus to ambiguous
samples and non-target logits in model learning. Through a deep analysis of the
task-recency bias caused by class imbalance, we propose a revised focal loss to
mainly keep stability. \Rt{By utilizing a new weight function, the revised
focal loss will pay more attention to current ambiguous samples, which are the
potentially valuable samples to make model progress quickly.} To promote
plasticity, we introduce a virtual knowledge distillation. By designing a
virtual teacher, it assigns more attention to non-target classes, which can
surmount overconfidence and encourage model to focus on inter-class
information. Extensive experiments on three popular datasets for OCIL have
shown the effectiveness of AFS. The code will be available at
\url{https://github.com/czjghost/AFS}.
