Abstract: The methods based on consistency regularization show better performance in semi-supervised semantic segmentation task.Such methods usually involve two roles,an explicit or implicit teacher network,and a student network which is trained by minimizing the consistency loss between the prediction results of two networks for different perturbation samples.But unreliable predictions from a single-teacher network may cause the student network to learn wrong information.By extending the mean teacher(MT) model to the multiple teacher network,multiplemeanteacher network(MMTNet) is proposed to make the student network learn from the average prediction results of multiple teacher networks,which can effectively reduce the impact of single-teacher network prediction errors.In addition,MMTNet implements data perturbation of unlabeled data by applying strong data augmentation and weak data augmentation to the unlabeled data,which increases the diversity of unlabeled data,alleviates the coupling problem between student network and teacher network to a certain extent and avoids the overfitting of student network to teacher network,so as to further reduce the impact of pseudo-label prediction errors in the teacher network.Experimental results on VOC 2012 augmented dataset show that the proposed multiple mean teacher network model MMTNet can achieve higher mean intersection over union than other mainstream semi-supervised semantic segmentation methods,and the actual segmentation performance is better.

Key words: Semi-supervised learning, Semantic segmentation, Mean teacher model, Multi-teacher network, Consistency regularization