[论文解读] Structured Consistency Loss for semi-supervised semantic segmentation

JongMok Kim, Joo Young Jang+2 more|arXiv (Cornell University)|Jan 14, 2020

Domain Adaptation and Few-Shot Learning参考文献 31被引用 47

一句话总结

本文提出了一种结构化一致性损失，考虑像素间相关性以增强半监督语义分割，并通过 CutMix 提高效率，在 Cityscapes 基准测试中取得了最佳结果。

ABSTRACT

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.

研究动机与目标

• 通过利用具有像素间相关性的一致性损失来促进半监督语义分割。
• 引入一种结构化一致性损失，以提高像素级分割的计算效率。
• 在 Cityscapes 验证集和测试集上展示最先进的性能。
• 证明该方法在 Cityscapes 基准测试的像素级语义标注中排名第一。

提出的方法

• 提出一种结构化一致性损失，它建模像素间相关性，而不仅仅是匹配的像素对。
• 将该损失与 CutMix 集成以降低计算复杂性。
• 旨在提升语义分割的半监督学习的效率与效果。
• 在 Cityscapes 基准数据（验证集和测试集）上展示经验性提升，并给出报告的 mIoU 分数。

实验结果

研究问题

• RQ1 能够捕捉像素间关系的结构化一致性损失是否能提升半监督语义分割的性能？
• RQ2 将所提出的损失与 CutMix 相结合是否在保持或提升准确性的同时降低计算复杂度？
• RQ3 在半监督设置下，所提方法对 Cityscapes 基准指标的影响是什么？

主要发现

• 在 Cityscapes 验证数据上达到 81.9 mIoU。
• 在 Cityscapes 测试数据上达到 83.84 mIoU。