Significant progress has been made in Video Object Segmentation (VOS), the
video object tracking task in its finest level. While the VOS task can be
naturally decoupled into image semantic segmentation and video object tracking,
significantly much more research effort has been made in segmentation than
tracking. In this paper, we introduce "tracking-by-detection" into VOS which
can coherently integrate segmentation into tracking, by proposing a new
temporal aggregation network and a novel dynamic time-evolving template
matching mechanism to achieve significantly improved performance. Notably, our
method is entirely online and thus suitable for one-shot learning, and our
end-to-end trainable model allows multiple object segmentation in one forward
pass. We achieve new state-of-the-art performance on the DAVIS benchmark
without complicated bells and whistles in both speed and accuracy, with a speed
of 0.14 second per frame and J&F measure of 75.9% respectively.

本文介绍了一种将 “tracking-by-detection” 引入视频对象分割的方法，通过提出一种新的时间聚合网络和新的动态时间演进模板匹配机制，成功将分割与跟踪相一致，并在 DAVIS 基准测试中取得了新的最优表现。