  This paper proposes a novel deep learning architecture for semantic
segmentation. The proposed Global and Selective Attention Network (GSANet)
features Atrous Spatial Pyramid Pooling (ASPP) with a novel sparsemax global
attention and a novel selective attention that deploys a condensation and
diffusion mechanism to aggregate the multi-scale contextual information from
the extracted deep features. A selective attention decoder is also proposed to
process the GSA-ASPP outputs for optimizing the softmax volume. We are the
first to benchmark the performance of semantic segmentation networks with the
low-complexity feature extraction network (FXN) MobileNetEdge, that is
optimized for low latency on edge devices. We show that GSANet can result in
more accurate segmentation with MobileNetEdge, as well as with strong FXNs,
such as Xception. GSANet improves the state-of-art semantic segmentation
accuracy on both the ADE20k and the Cityscapes datasets.
