A common challenge posed to robust semantic segmentation is the expensive data annotation cost. Existing semi-supervised solutions show great potential for solving this problem. Their key idea is constructing consistency regularization with unsupervised data augmentation from unlabeled data for model training. The perturbations for unlabeled data enable the consistency training loss, which benefits semi-supervised semantic segmentation. However, these perturbations destroy image context and introduce unnatural boundaries, which is harmful for semantic segmentation. Besides, the widely adopted semi-supervised learning framework, i.e. mean-teacher, suffers performance limitation since the student model finally converges to the teacher model. In this paper, first of all, we propose a context friendly differentiable geometric warping to conduct unsupervised data augmentation; secondly, a novel adversarial dual-student framework is proposed to improve the Mean-Teacher from the following two aspects: (1) dual student models are learned independently except for a stabilization constraint to encourage exploiting model diversities; (2) adversarial training scheme is applied to both students and the discriminators are resorted to distinguish reliable pseudo-label of unlabeled data for self-training. Effectiveness is validated via extensive experiments on PASCAL VOC2012 and Cityscapes. Our solution significantly improves the performance and state-of-the-art results are achieved on both datasets. Remarkably, compared with fully supervision, our solution achieves comparable mIoU of 73.4% using only 12.5% annotated data on PASCAL VOC2012. Our codes and models are available at https://github.com/cao-cong/ADS-SemiSeg.
翻译:对稳健的语义分解提出的一个共同挑战是昂贵的数据解析成本。现有的半监督的解决方案显示了解决这一问题的巨大潜力。它们的关键理念是用无标签的模型培训数据来建立一致性规范化,从没有标签的模型培训数据中增加不受监督的数据。对于未贴标签的数据的扰动使一致性培训损失得以实现,这有利于半监督的语义分解。然而,这些扰动破坏了图像背景,引入了反常的界限,这对语义分解有害。此外,广泛采用的半监督的学习框架,即:平均教师,由于学生模式最终会与教师模式交汇,因此存在绩效限制。首先,我们提出了一种环境友好的、可监督的、可监督的、可监督的、测量的、可操作的数据分解的方法,以便进行不受监督的数据扩增;其次,提出了一个新的对抗性双重研究框架,以便从以下两个方面改进感官:(1) 独立学习双学生模式,但鼓励利用模式的脱差限制除外;(2) 对学生适用可比较的监管培训计划,在学生中应用可比较的S-alal-alal-al-alalalal-alation A.