  Pixel-wise semantic segmentation for visual scene understanding not only
needs to be accurate, but also efficient in order to find any use in real-time
application. Existing algorithms even though are accurate but they do not focus
on utilizing the parameters of neural network efficiently. As a result they are
huge in terms of parameters and number of operations; hence slow too. In this
paper, we propose a novel deep neural network architecture which allows it to
learn without any significant increase in number of parameters. Our network
uses only 11.5 million parameters and 21.2 GFLOPs for processing an image of
resolution 3x640x360. It gives state-of-the-art performance on CamVid and
comparable results on Cityscapes dataset. We also compare our networks
processing time on NVIDIA GPU and embedded system device with existing
state-of-the-art architectures for different image resolutions.
