  Semi-supervised semantic segmentation aims to learn from a small amount of
labeled data and plenty of unlabeled ones for the segmentation task. The most
common approach is to generate pseudo-labels for unlabeled images to augment
the training data. However, the noisy pseudo-labels will lead to cumulative
classification errors and aggravate the local inconsistency in prediction. This
paper proposes a Region Relevance Network (RRN) to alleviate the problem
mentioned above. Specifically, we first introduce a local pseudo-label
filtering module that leverages discriminator networks to assess the accuracy
of the pseudo-label at the region level. A local selection loss is proposed to
mitigate the negative impact of wrong pseudo-labels in consistency
regularization training. In addition, we propose a dynamic region-loss
correction module, which takes the merit of network diversity to further rate
the reliability of pseudo-labels and correct the convergence direction of the
segmentation network with a dynamic region loss. Extensive experiments are
conducted on PASCAL VOC 2012 and Cityscapes datasets with varying amounts of
labeled data, demonstrating that our proposed approach achieves
state-of-the-art performance compared to current counterparts.
