  Video semantic segmentation has achieved great progress under the supervision
of large amounts of labelled training data. However, domain adaptive video
segmentation, which can mitigate data labelling constraints by adapting from a
labelled source domain toward an unlabelled target domain, is largely
neglected. We design temporal pseudo supervision (TPS), a simple and effective
method that explores the idea of consistency training for learning effective
representations from unlabelled target videos. Unlike traditional consistency
training that builds consistency in spatial space, we explore consistency
training in spatiotemporal space by enforcing model consistency across
augmented video frames which helps learn from more diverse target data.
Specifically, we design cross-frame pseudo labelling to provide pseudo
supervision from previous video frames while learning from the augmented
current video frames. The cross-frame pseudo labelling encourages the network
to produce high-certainty predictions, which facilitates consistency training
with cross-frame augmentation effectively. Extensive experiments over multiple
public datasets show that TPS is simpler to implement, much more stable to
train, and achieves superior video segmentation accuracy as compared with the
state-of-the-art.
