  Modelling long-range dependencies is critical for scene understanding tasks
in computer vision. Although CNNs have excelled in many vision tasks, they are
still limited in capturing long-range structured relationships as they
typically consist of layers of local kernels. A fully-connected graph is
beneficial for such modelling, however, its computational overhead is
prohibitive. We propose a dynamic graph message passing network, that
significantly reduces the computational complexity compared to related works
modelling a fully-connected graph. This is achieved by adaptively sampling
nodes in the graph, conditioned on the input, for message passing. Based on the
sampled nodes, we dynamically predict node-dependent filter weights and the
affinity matrix for propagating information between them. Using this model, we
show significant improvements with respect to strong, state-of-the-art
baselines on three different tasks and backbone architectures. Our approach
also outperforms fully-connected graphs while using substantially fewer
floating-point operations and parameters. The project website is
http://www.robots.ox.ac.uk/~lz/dgmn/
