Semi-Supervised Semantic Segmentation With Cross Pseudo Supervision

Xiaokang Chen, Yuhui Yuan, Gang Zeng, Jingdong Wang; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 2613-2622

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.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Chen_2021_CVPR, author = {Chen, Xiaokang and Yuan, Yuhui and Zeng, Gang and Wang, Jingdong}, title = {Semi-Supervised Semantic Segmentation With Cross Pseudo Supervision}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {2613-2622} }