Learning Video Instance Segmentation with Recurrent Graph Neural Networks

Understand

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.