Structured Consistency Loss for semi-supervised semantic segmentation
This work addresses the problem of improving semi-supervised learning for semantic segmentation, which is incremental as it builds on existing consistency loss methods by incorporating structured predictions.
The paper tackled the problem of semi-supervised semantic segmentation by proposing a structured consistency loss to address limitations in existing methods that rely on pixel-wise classification, achieving state-of-the-art results with 81.9 mIoU on validation and 83.84 mIoU on test data on the Cityscapes benchmark.
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.