  We propose a network architecture to perform efficient scene understanding.
This work presents three main novelties: the first is an Improved Guided
Upsampling Module that can replace in toto the decoder part in common semantic
segmentation networks. Our second contribution is the introduction of a new
module based on spatial sampling to perform Instance Segmentation. It provides
a very fast instance segmentation, needing only thresholding as post-processing
step at inference time. Finally, we propose a novel efficient network design
that includes the new modules and test it against different datasets for
outdoor scene understanding. To our knowledge, our network is one of the
themost efficient architectures for scene understanding published to date,
furthermore being 8.6% more accurate than the fastest competitor on semantic
segmentation and almost five times faster than the most efficient network for
instance segmentation.
