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).

ContextNet 提出了一种新的深度神经网络架构，利用分解卷积、网络压缩和金字塔表示等技术，实现了高效的全局上下文信息提取和高分辨率分割细节捕捉，从而在保持精度的前提下，降低了内存需求，实现了具备实时性和低存储成本的语义分割。