  Video instance segmentation is a challenging task that extends image instance
segmentation to the video domain. Existing methods either rely only on
single-frame information for the detection and segmentation subproblems or
handle tracking as a separate post-processing step, which limit their
capability to fully leverage and share useful spatial-temporal information for
all the subproblems. In this paper, we propose a novel graph-neural-network
(GNN) based method to handle the aforementioned limitation. Specifically, graph
nodes representing instance features are used for detection and segmentation
while graph edges representing instance relations are used for tracking. Both
inter and intra-frame information is effectively propagated and shared via
graph updates and all the subproblems (i.e. detection, segmentation and
tracking) are jointly optimized in an unified framework. The performance of our
method shows great improvement on the YoutubeVIS validation dataset compared to
existing methods and achieves 35.2% AP with a ResNet-50 backbone, operating at
22 FPS. Code is available at http://github.com/lucaswithai/visgraph.git .
