Training deep networks for semantic segmentation requires large amounts of
labeled training data, which presents a major challenge in practice, as
labeling segmentation masks is a highly labor-intensive process. To address
this issue, we present a framework for semi-supervised semantic segmentation,
which is enhanced by self-supervised monocular depth estimation from unlabeled
image sequences. In particular, we propose three key contributions: (1) We
transfer knowledge from features learned during self-supervised depth
estimation to semantic segmentation, (2) we implement a strong data
augmentation by blending images and labels using the geometry of the scene, and
(3) we utilize the depth feature diversity as well as the level of difficulty
of learning depth in a student-teacher framework to select the most useful
samples to be annotated for semantic segmentation. We validate the proposed
model on the Cityscapes dataset, where all three modules demonstrate
significant performance gains, and we achieve state-of-the-art results for
semi-supervised semantic segmentation. The implementation is available at
this https URL

提出了一种半监督语义分割的框架，采用自监督单目深度估计和利用场景几何形状强数据扩增的方式，结合深度特征差异和难度级别，通过学生 - 教师框架选择最有用的样本进行语义分割的注释，实现了在 Cityscapes 数据集上半监督语义分割的最好结果。