论文标题
半监督语义细分的区域级对比度和一致性学习
Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation
论文作者
论文摘要
当前的半监督语义分割方法主要集中于设计像素级的一致性和对比度正则化。但是，像素级正则化对具有错误预测的像素的噪声敏感，并且像素级对比度正则化具有O（Pixel_num^2）的内存和计算成本。为了解决这些问题，我们为半监督语义分割提出了一个新颖的区域级对比度和一致性学习框架（RC^2L）。具体而言，我们首先提出区域面具对比度（RMC）损失和区域特征对比度（RFC）损失，以完成区域级的对比特性。此外，为实现区域级的一致性提出了区域类别一致性（RCC）损失和语义面具一致性（SMC）损失。根据提议的区域级对比度和一致性正则化，我们开发了一个区域级的对比度和一致性学习框架（RC^2L），用于半监视语义分割，并评估我们的RC $^2 $ L在两个具有挑战性的基准（Pascal VOC 2012和CityScapes）上，超过了您的状态。
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.
