In this paper, we present a simple but performant semi-supervised semantic
segmentation approach, termed CorrMatch. Our goal is to mine more high-quality
regions from the unlabeled images to leverage the unlabeled data more
efficiently via consistency regularization. The key contributions of our
CorrMatch are two novel and complementary strategies. First, we introduce an
adaptive threshold updating strategy with a relaxed initialization to expand
the high-quality regions. Furthermore, we propose to propagate high-confidence
predictions through measuring the pairwise similarities between pixels. Despite
its simplicity, we show that CorrMatch achieves great performance on popular
semi-supervised semantic segmentation benchmarks. Taking the DeepLabV3+
framework with ResNet-101 backbone as our segmentation model, we receive a 76%+
mIoU score on the Pascal VOC 2012 segmentation benchmark with only 92 annotated
images provided. We also achieve a consistent improvement over previous
semi-supervised semantic segmentation models. Code will be made publicly
available.

本研究提出了一个针对半监督语义分割的简单但高效的半监督语义分割方法，称为 CorrMatch，目的是通过一致性正则化从未标记的图像中挖掘更多高质量的区域，以更有效地利用未标记的数据。