  Semi-supervised video object segmentation (VOS) aims to segment arbitrary
target objects in video when the ground truth segmentation mask of the initial
frame is provided. Due to this limitation of using prior knowledge about the
target object, feature matching, which compares template features representing
the target object with input features, is an essential step. Recently,
pixel-level matching (PM), which matches every pixel in template features and
input features, has been widely used for feature matching because of its high
performance. However, despite its effectiveness, the information used to build
the template features is limited to the initial and previous frames. We address
this issue by proposing a novel method-PM-based video object segmentation
(PMVOS)-that constructs strong template features containing the information of
all past frames. Furthermore, we apply self-attention to the similarity maps
generated from PM to capture global dependencies. On the DAVIS 2016 validation
set, we achieve new state-of-the-art performance among real-time methods (> 30
fps), with a J&F score of 85.6%. Performance on the DAVIS 2017 and YouTube-VOS
validation sets is also impressive, with J&F scores of 74.0% and 68.2%,
respectively.
