Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
This work addresses the problem of reducing annotation costs for semantic segmentation in computer vision, presenting an incremental improvement over existing semi-supervised methods.
The paper tackles semi-supervised semantic segmentation by proposing cross pseudo supervision (CPS), a consistency regularization method that uses pseudo labels from two perturbed networks to supervise each other, achieving state-of-the-art performance on Cityscapes and PASCAL VOC 2012 datasets.
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.