  The encoder-decoder framework is state-of-the-art for offline semantic image
segmentation. Since the rise in autonomous systems, real-time computation is
increasingly desirable. In this paper, we introduce fast segmentation
convolutional neural network (Fast-SCNN), an above real-time semantic
segmentation model on high resolution image data (1024x2048px) suited to
efficient computation on embedded devices with low memory. Building on existing
two-branch methods for fast segmentation, we introduce our `learning to
downsample' module which computes low-level features for multiple resolution
branches simultaneously. Our network combines spatial detail at high resolution
with deep features extracted at lower resolution, yielding an accuracy of 68.0%
mean intersection over union at 123.5 frames per second on Cityscapes. We also
show that large scale pre-training is unnecessary. We thoroughly validate our
metric in experiments with ImageNet pre-training and the coarse labeled data of
Cityscapes. Finally, we show even faster computation with competitive results
on subsampled inputs, without any network modifications.
