Weakly-Supervised Semantic Segmentation with Mean Teacher Learning

• Published in: Intelligence Science and Big Data Engineering. Visual Data Engineering Vol. 11935; pp. 324 - 335
• Main Authors: Tan, Li, Luo, WenFeng, Yang, Meng
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Deep learning; Image semantic segmentation; Weakly-supervised learning
• ISBN: 3030361888; 9783030361884
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-36189-1_27

Weakly-supervised semantic segmentation with image-level labels is a important task as it directly associates high-level semantic to low-level appearance, which can significantly reduce human efforts. Despite the remarkable progress, it is still not as good as fully supervised segmentation methods. To improve the accuracy, in this paper, we proposed a novel framework of weakly-supervised semantic segmentation with mean teacher (WSSS-MT) learning to advance the class estimation of image pixels. More specifically, our proposed framework includes a student network and a teacher network in the segmentation module, which aims to effectively utilize information of the training process. The student learns the semantic segmentation network with an updated supervision, while the teacher uses the exponential moving average of the student to achieve a more accurate estimation of supervision. WSSS-MT employs the trained teacher as final segmentation network. Experimental results on the PASCAL VOC 2012 dataset show that the performance of our framework is better than the competing methods.