The problem of video object segmentation can become extremely challenging
when multiple instances co-exist. While each instance may exhibit large scale
and pose variations, the problem is compounded when instances occlude each
other causing failures in tracking. In this study, we formulate a deep
recurrent network that is capable of segmenting and tracking objects in video
simultaneously by their temporal continuity, yet able to re-identify them when
they re-appear after a prolonged occlusion. We combine both temporal
propagation and re-identification functionalities into a single framework that
can be trained end-to-end. In particular, we present a re-identification module
with template expansion to retrieve missing objects despite their large
appearance changes. In addition, we contribute a new attention-based recurrent
mask propagation approach that is robust to distractors not belonging to the
target segment. Our approach achieves a new state-of-the-art global mean
(Region Jaccard and Boundary F measure) of 68.2 on the challenging DAVIS 2017
benchmark (test-dev set), outperforming the winning solution which achieves a
global mean of 66.1 on the same partition.

本研究提出了一种使用深度循环网络实现同时分割和追踪视频中物体的方法，结合了时间传播和重新识别模块，以及基于注意力的循环蒙版传播方法，取得了在 DAVIS 2017 基准测试（test-dev 集合）上最高的分割和边缘测量平均值（68.2），优于同一分区上获胜解决方案的全球平均值（66.1）