Existing video object segmentation (VOS) benchmarks focus on short-term videos which just last about 3-5 seconds and where objects are visible most of the time. These videos are poorly representative of practical applications, and the absence of long-term datasets restricts further investigation of VOS on the application in realistic scenarios. So, in this paper, we present a new benchmark dataset named \textbf{LVOS}, which consists of 220 videos with a total duration of 421 minutes. To the best of our knowledge, LVOS is the first densely annotated long-term VOS dataset. The videos in our LVOS last 1.59 minutes on average, which is 20 times longer than videos in existing VOS datasets. Each video includes various attributes, especially challenges deriving from the wild, such as long-term reappearing and cross-temporal similar objeccts.Based on LVOS, we assess existing video object segmentation algorithms and propose a Diverse Dynamic Memory network (DDMemory) that consists of three complementary memory banks to exploit temporal information adequately. The experimental results demonstrate the strength and weaknesses of prior methods, pointing promising directions for further study. Data and code are available at https://lingyihongfd.github.io/lvos.github.io/.

翻译：现有视频目标分割基准数据集主要针对时长仅约3-5秒且目标在大多数时间内可见的短视频。这些视频难以代表实际应用场景，而长期数据集的缺失限制了视频目标分割在真实场景中应用的研究。为此，本文提出名为**LVOS**的全新基准数据集，包含220个视频片段，总时长达421分钟。据我们所知，LVOS是首个密集标注的长期视频目标分割数据集。数据集中视频平均时长为1.59分钟，是现有视频目标分割数据集的20倍。每个视频包含多种属性，尤其是来自野外的挑战性特征，如长期重现和目标跨时间相似性。基于LVOS，我们评估了现有视频目标分割算法，并提出一种由三个互补存储库组成的分集动态存储网络以充分利用时序信息。实验结果表明了现有方法的优势与不足，为后续研究指明了方向。数据和代码开源地址：https://lingyihongfd.github.io/lvos.github.io/。