The KITTI Vision Benchmark Suite

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.

Details can be found here:
Arxiv Link: https://arxiv.org/abs/1812.01593

@inproceedings{semantic_cvpr19,
author = {Yi Zhu*, Karan Sapra*, Fitsum
A. Reda, Kevin J. Shih, Shawn Newsam, Andrew
Tao, Bryan Catanzaro},
title = {Improving Semantic
Segmentation via Video Propagation and Label
Relaxation},
booktitle = {IEEE Conference on Computer
Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2019},
url = {https://nv-
adlr.github.io/publication/2018-Segmentation}
}