  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.
