Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets. State-of-the-art methods are, however, not directly transferable to real-time applications or embedded devices, since naive adaptation of such systems to reduce computational cost (speed, memory and energy) causes a significant drop in accuracy. We propose ContextNet, a new deep neural network architecture which builds on factorized convolution, network compression and pyramid representation to produce competitive semantic segmentation in real-time with low memory requirement. ContextNet combines a deep network branch at low resolution that captures global context information efficiently with a shallow branch that focuses on high-resolution segmentation details. We analyse our network in a thorough ablation study and present results on the Cityscapes dataset, achieving 66.1% accuracy at 18.3 frames per second at full (1024x2048) resolution (41.9 fps with pipelined computations for streamed data).
@article{arxiv.1805.04554,
title = {ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time},
author = {Rudra P K Poudel and Ujwal Bonde and Stephan Liwicki and Christopher Zach},
journal= {arXiv preprint arXiv:1805.04554},
year = {2018}
}
Comments
Published as a conference paper at British Machine Vision Conference (BMVC), 2018