  Semantic image segmentation is a central and challenging task in autonomous
driving, addressed by training deep models. Since this training draws to a
curse of human-based image labeling, using synthetic images with automatically
generated labels together with unlabeled real-world images is a promising
alternative. This implies to address an unsupervised domain adaptation (UDA)
problem. In this paper, we propose a new co-training procedure for
synth-to-real UDA of semantic segmentation models. It consists of a
self-training stage, which provides two domain-adapted models, and a model
collaboration loop for the mutual improvement of these two models. These models
are then used to provide the final semantic segmentation labels (pseudo-labels)
for the real-world images. The overall procedure treats the deep models as
black boxes and drives their collaboration at the level of pseudo-labeled
target images, i.e., neither modifying loss functions is required, nor explicit
feature alignment. We test our proposal on standard synthetic and real-world
datasets for on-board semantic segmentation. Our procedure shows improvements
ranging from ~13 to ~26 mIoU points over baselines, so establishing new
state-of-the-art results.
