  Recent success of semantic segmentation approaches on demanding road driving
datasets has spurred interest in many related application fields. Many of these
applications involve real-time prediction on mobile platforms such as cars,
drones and various kinds of robots. Real-time setup is challenging due to
extraordinary computational complexity involved. Many previous works address
the challenge with custom lightweight architectures which decrease
computational complexity by reducing depth, width and layer capacity with
respect to general purpose architectures. We propose an alternative approach
which achieves a significantly better performance across a wide range of
computing budgets. First, we rely on a light-weight general purpose
architecture as the main recognition engine. Then, we leverage light-weight
upsampling with lateral connections as the most cost-effective solution to
restore the prediction resolution. Finally, we propose to enlarge the receptive
field by fusing shared features at multiple resolutions in a novel fashion.
Experiments on several road driving datasets show a substantial advantage of
the proposed approach, either with ImageNet pre-trained parameters or when we
learn from scratch. Our Cityscapes test submission entitled SwiftNetRN-18
delivers 75.5% MIoU and achieves 39.9 Hz on 1024x2048 images on GTX1080Ti.
