  In this paper, we study the semi-supervised semantic segmentation problem via
exploring both labeled data and extra unlabeled data. We propose a novel
consistency regularization approach, called cross pseudo supervision (CPS). Our
approach imposes the consistency on two segmentation networks perturbed with
different initialization for the same input image. The pseudo one-hot label
map, output from one perturbed segmentation network, is used to supervise the
other segmentation network with the standard cross-entropy loss, and vice
versa. The CPS consistency has two roles: encourage high similarity between the
predictions of two perturbed networks for the same input image, and expand
training data by using the unlabeled data with pseudo labels. Experiment
results show that our approach achieves the state-of-the-art semi-supervised
segmentation performance on Cityscapes and PASCAL VOC 2012. Code is available
at https://git.io/CPS.
