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.

本文提出了一种基于时序伪监督的方法（TPS）来进行视频语义分割，该方法可以在适应有标签的源域到无标签的目标域时，通过跨视频帧产生伪标签的方式缓解数据标注的限制，从而提升了处理多样性目标数据时的准确率。实验证明，TPS 比现有技术更稳定，更简单易实现，且具有更高的视频语义分割准确率。