ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image Segmentation

Xinyue Huo, Lingxi Xie, Jianzhong He, Zijie Yang, Wengang Zhou, Houqiang Li, Qi Tian; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 1235-1244

Abstract


Semi-supervised learning is a useful tool for image segmentation, mainly due to its ability in extracting knowledge from unlabeled data to assist learning from labeled data. This paper focuses on a popular pipeline known as self-learning, where we point out a weakness named lazy mimicking that refers to the inertia that a model retains the prediction from itself and thus resists updates. To alleviate this issue, we propose the Asynchronous Teacher-Student Optimization (ATSO) algorithm that (i) breaks up continual learning from teacher to student and (ii) partitions the unlabeled training data into two subsets and alternately uses one subset to fine-tune the model which updates the labels on the other. We show the ability of ATSO on medical and natural image segmentation. In both scenarios, our method reports competitive performance, on par with the state-of-the-arts, in either using partial labeled data in the same dataset or transferring the trained model to an unlabeled dataset.

Related Material


[pdf] [supp]
[bibtex]
@InProceedings{Huo_2021_CVPR, author = {Huo, Xinyue and Xie, Lingxi and He, Jianzhong and Yang, Zijie and Zhou, Wengang and Li, Houqiang and Tian, Qi}, title = {ATSO: Asynchronous Teacher-Student Optimization for Semi-Supervised Image Segmentation}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2021}, pages = {1235-1244} }