  Semantic segmentation requires large amounts of pixel-wise annotations to
learn accurate models. In this paper, we present a video prediction-based
methodology to scale up training sets by synthesizing new training samples in
order to improve the accuracy of semantic segmentation networks. We exploit
video prediction models' ability to predict future frames in order to also
predict future labels. A joint propagation strategy is also proposed to
alleviate mis-alignments in synthesized samples. We demonstrate that training
segmentation models on datasets augmented by the synthesized samples leads to
significant improvements in accuracy. Furthermore, we introduce a novel
boundary label relaxation technique that makes training robust to annotation
noise and propagation artifacts along object boundaries. Our proposed methods
achieve state-of-the-art mIoUs of 83.5% on Cityscapes and 82.9% on CamVid. Our
single model, without model ensembles, achieves 72.8% mIoU on the KITTI
semantic segmentation test set, which surpasses the winning entry of the ROB
challenge 2018. Our code and videos can be found at
https://nv-adlr.github.io/publication/2018-Segmentation.
