  Most current semantic segmentation approaches fall back on deep convolutional
neural networks (CNNs). However, their use of convolution operations with local
receptive fields causes failures in modeling contextual spatial relations.
Prior works have sought to address this issue by using graphical models or
spatial propagation modules in networks. But such models often fail to capture
long-range spatial relationships between entities, which leads to spatially
fragmented predictions. Moreover, recent works have demonstrated that
channel-wise information also acts a pivotal part in CNNs. In this work, we
introduce two simple yet effective network units, the spatial relation module
and the channel relation module, to learn and reason about global relationships
between any two spatial positions or feature maps, and then produce
relation-augmented feature representations. The spatial and channel relation
modules are general and extensible, and can be used in a plug-and-play fashion
with the existing fully convolutional network (FCN) framework. We evaluate
relation module-equipped networks on semantic segmentation tasks using two
aerial image datasets, which fundamentally depend on long-range spatial
relational reasoning. The networks achieve very competitive results, bringing
significant improvements over baselines.
