  Exploiting long-range contextual information is key for pixel-wise prediction
tasks such as semantic segmentation. In contrast to previous work that uses
multi-scale feature fusion or dilated convolutions, we propose a novel
graph-convolutional network (GCN) to address this problem. Our Dual Graph
Convolutional Network (DGCNet) models the global context of the input feature
by modelling two orthogonal graphs in a single framework. The first component
models spatial relationships between pixels in the image, whilst the second
models interdependencies along the channel dimensions of the network's feature
map. This is done efficiently by projecting the feature into a new,
lower-dimensional space where all pairwise interactions can be modelled, before
reprojecting into the original space. Our simple method provides substantial
benefits over a strong baseline and achieves state-of-the-art results on both
Cityscapes (82.0% mean IoU) and Pascal Context (53.7% mean IoU) datasets. Code
and models are made available to foster any further research
(\url{https://github.com/lxtGH/GALD-DGCNet}).
