In this paper we present our system for human-in-the-loop video object
segmentation. The backbone of our system is a method for one-shot video object
segmentation. While fast, this method requires an accurate pixel-level
segmentation of one (or several) frames as input. As manually annotating such a
segmentation is impractical, we propose a deep interactive image segmentation
method, that can accurately segment objects with only a handful of clicks. On
the GrabCut dataset, our method obtains 90% IOU with just 3.8 clicks on
average, setting the new state of the art. Furthermore, as our method
iteratively refines an initial segmentation, it can effectively correct frames
where the video object segmentation fails, thus allowing users to quickly
obtain high quality results even on challenging sequences. Finally, we
investigate usage patterns and give insights in how many steps users take to
annotate frames, what kind of corrections they provide, etc., thus giving
important insights for further improving interactive video segmentation.

本文介绍一种基于人工交互的视频对象分割方法，使用深度学习技术进行交互式图像分割，并应用于视频对象分割中。在 GrabCut 数据集上，我们的方法以仅需 3.8 次点击的平均值获得 90％的 IOU，与当前最先进的方法相比，具有更高的准确性。此外，我们研究了用户的使用模式和提供的纠正类型等，为进一步改进交互式视频分割提供重要的见解。