  Unsupervised Domain Adaptation for semantic segmentation has gained immense
popularity since it can transfer knowledge from simulation to real (Sim2Real)
by largely cutting out the laborious per pixel labeling efforts at real. In
this work, we present a new video extension of this task, namely Unsupervised
Domain Adaptation for Video Semantic Segmentation. As it became easy to obtain
large-scale video labels through simulation, we believe attempting to maximize
Sim2Real knowledge transferability is one of the promising directions for
resolving the fundamental data-hungry issue in the video. To tackle this new
problem, we present a novel two-phase adaptation scheme. In the first step, we
exhaustively distill source domain knowledge using supervised loss functions.
Simultaneously, video adversarial training (VAT) is employed to align the
features from source to target utilizing video context. In the second step, we
apply video self-training (VST), focusing only on the target data. To construct
robust pseudo labels, we exploit the temporal information in the video, which
has been rarely explored in the previous image-based self-training approaches.
We set strong baseline scores on 'VIPER to CityscapeVPS' adaptation scenario.
We show that our proposals significantly outperform previous image-based UDA
methods both on image-level (mIoU) and video-level (VPQ) evaluation metrics.

Computer Vision and Pattern Recognition