  This paper tackles the problem of real-time semantic segmentation of high
definition videos using a hybrid GPU / CPU approach. We propose an Efficient
Video Segmentation(EVS) pipeline that combines:
  (i) On the CPU, a very fast optical flow method, that is used to exploit the
temporal aspect of the video and propagate semantic information from one frame
to the next. It runs in parallel with the GPU.
  (ii) On the GPU, two Convolutional Neural Networks: A main segmentation
network that is used to predict dense semantic labels from scratch, and a
Refiner that is designed to improve predictions from previous frames with the
help of a fast Inconsistencies Attention Module (IAM). The latter can identify
regions that cannot be propagated accurately.
  We suggest several operating points depending on the desired frame rate and
accuracy. Our pipeline achieves accuracy levels competitive to the existing
real-time methods for semantic image segmentation(mIoU above 60%), while
achieving much higher frame rates. On the popular Cityscapes dataset with high
resolution frames (2048 x 1024), the proposed operating points range from 80 to
1000 Hz on a single GPU and CPU.
