论文标题
半监督语义分割的结构性一致性损失
Structured Consistency Loss for semi-supervised semantic segmentation
论文作者
论文摘要
在最近的半监督学习研究中，一致性损失在解决问题中起着关键作用。然而，现存的研究损失限于其在分类任务中的应用；关于半监督语义分割的现有研究依赖于像素的分类，这不反映预测中特征的结构化性质。我们提出了结构化的一致性损失，以解决现有研究的这一局限性。结构化的一致性损失促进了教师和学生网络之间像素间相似性的一致性。具体而言，与CutMix的合作通过大大减轻计算负担，优化了半监督语义分割的有效性能，并通过结构化的一致性损失进行了有效性能。用城市景观验证了提议的方法的优势；通过验证和测试数据的CityScapes基准结果分别为81.9 MIOU和83.84 MIOU。这是CityScapes基准套件的像素级语义标签任务的第一名。据我们所知，我们是第一个在语义细分中提出最先进的半监督学习的优势。
The consistency loss has played a key role in solving problems in recent studies on semi-supervised learning. Yet extant studies with the consistency loss are limited to its application to classification tasks; extant studies on semi-supervised semantic segmentation rely on pixel-wise classification, which does not reflect the structured nature of characteristics in prediction. We propose a structured consistency loss to address this limitation of extant studies. Structured consistency loss promotes consistency in inter-pixel similarity between teacher and student networks. Specifically, collaboration with CutMix optimizes the efficient performance of semi-supervised semantic segmentation with structured consistency loss by reducing computational burden dramatically. The superiority of proposed method is verified with the Cityscapes; The Cityscapes benchmark results with validation and with test data are 81.9 mIoU and 83.84 mIoU respectively. This ranks the first place on the pixel-level semantic labeling task of Cityscapes benchmark suite. To the best of our knowledge, we are the first to present the superiority of state-of-the-art semi-supervised learning in semantic segmentation.
