We propose an approach to semantic (image) segmentation that reduces the computational costs by a factor of 25 with limited impact on the quality of results. Semantic segmentation has a number of practical applications, and for most such applications the computational costs are critical. The method follows a typical two-column network structure, where one column accepts an input image, while the other accepts a half-resolution version of that image. By identifying specific regions in the full-resolution image that can be safely ignored, as well as carefully tailoring the network structure, we can process approximately 15 highresolution Cityscapes images (1024x2048) per second using a single GTX 980 video card, while achieving a mean intersection-over-union score of 72.9% on the Cityscapes test set.
@article{arxiv.1712.00213,
title = {Real-time Semantic Image Segmentation via Spatial Sparsity},
author = {Zifeng Wu and Chunhua Shen and Anton van den Hengel},
journal= {arXiv preprint arXiv:1712.00213},
year = {2017}
}