Learning from Future: A Novel Self-Training Framework for Semantic Segmentation

Self-training has shown great potential in semi-supervised learning. Its core idea is to use the model learned on labeled data to generate pseudo-labels for unlabeled samples, and in turn teach itself. To obtain valid supervision, active attempts typically employ a momentum teacher for pseudo-label prediction yet observe the confirmation bias issue, where the incorrect predictions may provide wrong supervision signals and get accumulated in the training process. The primary cause of such a drawback is that the prevailing self-training framework acts as guiding the current state with previous knowledge because the teacher is updated with the past student only. To alleviate this problem, we propose a novel self-training strategy, which allows the model to learn from the future. Concretely, at each training step, we first virtually optimize the student (i. e. , caching the gradients without applying them to the model weights), then update the teacher with the virtual future student, and finally ask the teacher to produce pseudo-labels for the current student as the guidance. In this way, we manage to improve the quality of pseudo-labels and thus boost the performance. We also develop two variants of our future-self-training (FST) framework through peeping at the future both deeply (FST-D) and widely (FST-W). Taking the tasks of unsupervised domain adaptive semantic segmentation and semi-supervised semantic segmentation as the instances, we experimentally demonstrate the effectiveness and superiority of our approach under a wide range of settings. Code is available at https: //github. com/usr922/FST.

Authors

• Ye Du Southwest University of Finance and Economics
• Yujun Shen
• Haochen Wang
• Jingjing Fei
• Wei Li
• Liwei Wu SenseTime Research
• Rui Zhao SenseTime Research
• Zehua Fu Hangzhou Innovation Institute, Beihang University, Hangzhou, China
• Qingjie Liu State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China Hangzhou Innovation Institute, Beihang University, Hangzhou, China Zhongguancun Laboratory, Beijing, China

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