Current semi-supervised semantic segmentation methods mainly focus on designing pixel-level consistency and contrastive regularization. However, pixel-level regularization is sensitive to noise from pixels with incorrect predictions, and pixel-level contrastive regularization has memory and computational cost with O(pixel_num^2). To address the issues, we propose a novel region-level contrastive and consistency learning framework (RC^2L) for semi-supervised semantic segmentation. Specifically, we first propose a Region Mask Contrastive (RMC) loss and a Region Feature Contrastive (RFC) loss to accomplish region-level contrastive property. Furthermore, Region Class Consistency (RCC) loss and Semantic Mask Consistency (SMC) loss are proposed for achieving region-level consistency. Based on the proposed region-level contrastive and consistency regularization, we develop a region-level contrastive and consistency learning framework (RC^2L) for semi-supervised semantic segmentation, and evaluate our RC$^2$L on two challenging benchmarks (PASCAL VOC 2012 and Cityscapes), outperforming the state-of-the-art.

本文提出了基于区域级别对比和一致性学习框架 (RC^2L) 的半监督语义分割方法，通过引入区域级别损失，包括 Region Mask Contrastive (RMC) 损失、Region Feature Contrastive (RFC) 损失、Region Class Consistency (RCC) 损失和 Semantic Mask Consistency (SMC) 损失，实现了更好的稳定性和性能。在 PASCAL VOC 2012 和 Cityscapes 数据集上的实验结果表明，所提出的方法优于现有最先进方法。

区域级别的对比一致性学习用于半监督语义分割