Co-Training for Unsupervised Domain Adaptation of Semantic Segmentation Models
This addresses the problem of reducing human labeling effort in autonomous driving by adapting models from synthetic to real-world data, representing a strong incremental advance in domain adaptation methods.
The paper tackles unsupervised domain adaptation for semantic segmentation by proposing a co-training procedure that uses synthetic and real-world images, achieving improvements of 13 to 26 mIoU points over baselines and setting new state-of-the-art results.
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.