Online continual learning (online CL) studies the problem of learning sequential tasks from an online data stream without task boundaries, aiming to adapt to new data while alleviating catastrophic forgetting on the past tasks. This paper proposes a framework Contrastive Vision Transformer (CVT), which designs a focal contrastive learning strategy based on a transformer architecture, to achieve a better stability-plasticity trade-off for online CL. Specifically, we design a new external attention mechanism for online CL that implicitly captures previous tasks' information. Besides, CVT contains learnable focuses for each class, which could accumulate the knowledge of previous classes to alleviate forgetting. Based on the learnable focuses, we design a focal contrastive loss to rebalance contrastive learning between new and past classes and consolidate previously learned representations. Moreover, CVT contains a dual-classifier structure for decoupling learning current classes and balancing all observed classes. The extensive experimental results show that our approach achieves state-of-the-art performance with even fewer parameters on online CL benchmarks and effectively alleviates the catastrophic forgetting.

本文提出了一种基于transformer架构的对比视觉转换器(CVT)框架，它设计了一种基于对比学习的强调学习策略，以实现在线学习的更好稳定性-可塑性权衡，并通过包含可学习的聚焦点来缓解遗忘。实验结果表明，CVT方法在在线持续学习基准测试中达到最先进的性能，并有效缓解了灾难性的遗忘。

基于对比视觉变换器的在线连续学习