In this paper we present a new computer vision task, named video instance
segmentation. The goal of this new task is simultaneous detection, segmentation
and tracking of instances in videos. In words, it is the first time that the
image instance segmentation problem is extended to the video domain. To
facilitate research on this new task, we propose a large-scale benchmark called
YouTube-VIS, which consists of 2883 high-resolution YouTube videos, a
40-category label set and 131k high-quality instance masks. In addition, we
propose a novel algorithm called MaskTrack R-CNN for this task. Our new method
introduces a new tracking branch to Mask R-CNN to jointly perform the
detection, segmentation and tracking tasks simultaneously. Finally, we evaluate
the proposed method and several strong baselines on our new dataset.
Experimental results clearly demonstrate the advantages of the proposed
algorithm and reveal insight for future improvement. We believe the video
instance segmentation task will motivate the community along the line of
research for video understanding.

本论文提出了一个新的计算机视觉任务，名为视频实例分割，目标是同时检测、分割和跟踪视频帧中的实例。为了便于研究，提出了一个大规模基准数据集 YouTube-VIS 和一个新算法 MaskTrack R-CNN。实验表明，这个算法优于强基线，并为未来研究提供了启示。