  Semi-supervised semantic segmentation relieves the reliance on large-scale
labeled data by leveraging unlabeled data. Recent semi-supervised semantic
segmentation approaches mainly resort to pseudo-labeling methods to exploit
unlabeled data. However, unreliable pseudo-labeling can undermine the
semi-supervision processes. In this paper, we propose an algorithm called
Multi-Level Label Correction (MLLC), which aims to use graph neural networks to
capture structural relationships in Semantic-Level Graphs (SLGs) and
Class-Level Graphs (CLGs) to rectify erroneous pseudo-labels. Specifically,
SLGs represent semantic affinities between pairs of pixel features, and CLGs
describe classification consistencies between pairs of pixel labels. With the
support of proximate pattern information from graphs, MLLC can rectify
incorrectly predicted pseudo-labels and can facilitate discriminative feature
representations. We design an end-to-end network to train and perform this
effective label corrections mechanism. Experiments demonstrate that MLLC can
significantly improve supervised baselines and outperforms state-of-the-art
approaches in different scenarios on Cityscapes and PASCAL VOC 2012 datasets.
Specifically, MLLC improves the supervised baseline by at least 5% and 2% with
DeepLabV2 and DeepLabV3+ respectively under different partition protocols.
