Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

Xiaokang Chen, Yuhui Yuan, Gang Zeng, Jingdong Wang

Abstract:

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.