  With the increasing demand of autonomous systems, pixelwise semantic
segmentation for visual scene understanding needs to be not only accurate but
also efficient for potential real-time applications. In this paper, we propose
Context Aggregation Network, a dual branch convolutional neural network, with
significantly lower computational costs as compared to the state-of-the-art,
while maintaining a competitive prediction accuracy. Building upon the existing
dual branch architectures for high-speed semantic segmentation, we design a
cheap high resolution branch for effective spatial detailing and a context
branch with light-weight versions of global aggregation and local distribution
blocks, potent to capture both long-range and local contextual dependencies
required for accurate semantic segmentation, with low computational overheads.
We evaluate our method on two semantic segmentation datasets, namely Cityscapes
dataset and UAVid dataset. For Cityscapes test set, our model achieves
state-of-the-art results with mIOU of 75.9%, at 76 FPS on an NVIDIA RTX 2080Ti
and 8 FPS on a Jetson Xavier NX. With regards to UAVid dataset, our proposed
network achieves mIOU score of 63.5% with high execution speed (15 FPS).
