  Most existing approaches to video instance segmentation comprise multiple
modules that are heuristically combined to produce the final output.
Formulating a purely learning-based method instead, which models both the
temporal aspect as well as a generic track management required to solve the
video instance segmentation task, is a highly challenging problem. In this
work, we propose a novel learning formulation, where the entire video instance
segmentation problem is modelled jointly. We fit a flexible model to our
formulation that, with the help of a graph neural network, processes all
available new information in each frame. Past information is considered and
processed via a recurrent connection. We demonstrate the effectiveness of the
proposed approach in comprehensive experiments. Our approach, operating at over
25 FPS, outperforms previous video real-time methods. We further conduct
detailed ablative experiments that validate the different aspects of our
approach.
