  The recent development of light-weighted neural networks has promoted the
applications of deep learning under resource constraints and mobile
applications. Many of these applications need to perform a real-time and
efficient prediction for semantic segmentation with a light-weighted network.
This paper introduces a light-weighted network with an efficient reduced
non-local module (LRNNet) for efficient and realtime semantic segmentation. We
proposed a factorized convolutional block in ResNet-Style encoder to achieve
more lightweighted, efficient and powerful feature extraction. Meanwhile, our
proposed reduced non-local module utilizes spatial regional dominant singular
vectors to achieve reduced and more representative non-local feature
integration with much lower computation and memory cost. Experiments
demonstrate our superior trade-off among light-weight, speed, computation and
accuracy. Without additional processing and pretraining, LRNNet achieves 72.2%
mIoU on Cityscapes test dataset only using the fine annotation data for
training with only 0.68M parameters and with 71 FPS on a GTX 1080Ti card.
