Continual learning in real-world scenarios is a major challenge. A general continual learning model should have a constant memory size and no predefined task boundaries, as is the case in semi-supervised Video Object Segmentation (VOS), where continual learning challenges particularly present themselves in working on long video sequences. In this article, we first formulate the problem of semi-supervised VOS, specifically online VOS, as a continual learning problem, and then secondly provide a public VOS dataset, CLVOS23, focusing on continual learning. Finally, we propose and implement a regularization-based continual learning approach on LWL, an existing online VOS baseline, to demonstrate the efficacy of continual learning when applied to online VOS and to establish a CLVOS23 baseline. We apply the proposed baseline to the Long Videos dataset as well as to two short video VOS datasets, DAVIS16 and DAVIS17. To the best of our knowledge, this is the first time that VOS has been defined and addressed as a continual learning problem.

翻译：现实场景中的持续学习是一项重大挑战。通用的持续学习模型应具备恒定内存大小且无需预定义任务边界，这在半监督视频目标分割领域尤为突出——持续学习难题在长视频序列处理中尤为显著。本文首先将半监督视频目标分割问题（特别是在线视频目标分割）形式化为持续学习问题，继而提出面向持续学习的公共视频目标分割数据集CLVOS23。最后，我们在现有在线视频目标分割基线方法LWL的基础上，设计并实现了一种基于正则化的持续学习方案，以验证持续学习在在线视频目标分割中的有效性，并为CLVOS23建立基准。我们将所提基线方法应用于长视频数据集以及两个短视频目标分割数据集（DAVIS16和DAVIS17）。据我们所知，这是首次将视频目标分割定义为持续学习问题并进行针对性解决。