Continual Learning is a step towards lifelong intelligence where models continuously learn from recently collected data without forgetting previous knowledge. Existing continual learning approaches mostly focus on image classification in the class-incremental setup with clear task boundaries and unlimited computational budget. This work explores the problem of Online Domain-Incremental Continual Segmentation (ODICS), where the model is continually trained over batches of densely labeled images from different domains, with limited computation and no information about the task boundaries. ODICS arises in many practical applications. In autonomous driving, this may correspond to the realistic scenario of training a segmentation model over time on a sequence of cities. We analyze several existing continual learning methods and show that they perform poorly in this setting despite working well in class-incremental segmentation. We propose SimCS, a parameter-free method complementary to existing ones that uses simulated data to regularize continual learning. Experiments show that SimCS provides consistent improvements when combined with different CL methods.

翻译：持续学习是实现终身智能的关键步骤，模型需在持续从新采集数据中学习的同时，避免遗忘先前知识。现有持续学习方法主要聚焦于具有明确任务边界和无限计算预算的类增量图像分类。本文探索在线域增量持续分割（ODICS）问题：模型需在有限计算资源且无任务边界信息的条件下，对来自不同领域的批量密集标注图像进行持续训练。ODICS在诸多实际应用中具有重要意义，例如在自动驾驶场景中，这对应于车辆随时间推移在多个城市序列上持续训练分割模型的现实需求。我们分析了多种现有持续学习方法，发现它们在类增量分割中表现优异，但在本场景下效果不佳。为此，我们提出SimCS——一种无需参数的通用增强方法，通过利用模拟数据对持续学习过程进行正则化约束。实验表明，SimCS在与不同持续学习方法相结合时均能带来稳定的性能提升。