Abstract Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets. State-ofthe-art methods are, however, not directly transferable to real-time applications or embedded devices, since naïve 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 (1024 × 2048) resolution (41.9 fps with pipelined computations for streamed data).
ContextNet REF combines a deep network branch at low resolution capturing global context efficiently with a shallow branch focusing on high-resolution segmentation details to reach competitive performance.
ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time
ArXiv
ArXiv
Computer Science