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Capturing global contextual representations by exploiting long-rangepixel-pixel dependencies has shown to improve semantic segmentationperformance. However, how to do this efficiently is an open question as currentapproaches of utilising attention schemes or very deep models to increase themodels field of view, result in complex models with large memory consumption.Inspired by recent work on graph neural networks, we propose theSelf-Constructing Graph (SCG) module that learns a long-range dependency graphdirectly from the image and uses it to propagate contextual informationefficiently to improve semantic segmentation. The module is optimised via anovel adaptive diagonal enhancement method and a variational lower bound thatconsists of a customized graph reconstruction term and a Kullback-Leiblerdivergence regularization term. When incorporated into a neural network(SCG-Net), semantic segmentation is performed in an end-to-end manner andcompetitive performance (mean F1-scores of 92.0% and 89.8% respectively) on thepublicly available ISPRS Potsdam and Vaihingen datasets is achieved, with muchfewer parameters, and at a lower computational cost compared to related pureconvolutional neural network (CNN) based models.

SCG-Net: Self-Constructing Graph Neural Networks for Semantic Segmentation