CLVOS23：一个用于持续学习的长视频目标分割数据集
CLVOS23: A Long Video Object Segmentation Dataset for Continual Learning
AI总结：
本文首次将半监督在线视频目标分割（VOS）形式化为持续学习问题，提出CLVOS23长视频数据集，并在LWL基线上实现基于正则化的持续学习方法，验证了其有效性。
摘要：
真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有恒定的内存大小且无预定义的任务边界，半监督视频目标分割（VOS）正是如此，其持续学习挑战主要体现在处理长视频序列时。在本文中，我们首先将半监督VOS（特别是在线VOS）问题形式化为持续学习问题，其次提供了一个专注于持续学习的公开VOS数据集CLVOS23。最后，我们在现有的在线VOS基线LWL上提出并实现了一种基于正则化的持续学习方法，以证明持续学习应用于在线VOS的有效性，并建立CLVOS23基线。我们将所提出的基线应用于长视频数据集以及两个短视频VOS数据集DAVIS16和DAVIS17。据我们所知，这是首次将VOS定义并作为持续学习问题来解决。
英文摘要：
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.
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。
