SimCS：面向领域增量在线持续分割的仿真
SimCS: Simulation for Domain Incremental Online Continual Segmentation
AI总结：
本文提出SimCS，一种无参数的在线领域增量持续分割方法，通过仿真数据正则化持续学习，解决自动驾驶等场景中无任务边界且计算受限的分割模型持续训练问题。
摘要：
持续学习是迈向终身智能的一步，模型在不遗忘先前知识的情况下持续从最近收集的数据中学习。现有的持续学习方法大多关注具有清晰任务边界和无限计算预算的类增量设置下的图像分类。本工作探索了在线领域增量持续分割（ODICS）问题，模型在不同领域的密集标注图像批次上持续训练，计算资源有限且无任务边界信息。ODICS出现在许多实际应用中。在自动驾驶中，这对应于随时间推移在一系列城市上训练分割模型的真实场景。我们分析了几种现有的持续学习方法，并表明尽管它们在类增量分割中表现良好，但在该设置下表现不佳。我们提出了SimCS，一种与现有方法互补的无参数方法，利用仿真数据来正则化持续学习。实验表明，SimCS与不同CL方法结合时能提供一致的改进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
