  This paper addresses semi-supervised semantic segmentation by exploiting a
small set of images with pixel-level annotations (strong supervisions) and a
large set of images with only image-level annotations (weak supervisions). Most
existing approaches aim to generate accurate pixel-level labels from weak
supervisions. However, we observe that those generated labels still inevitably
contain noisy labels. Motivated by this observation, we present a novel
perspective and formulate this task as a problem of learning with pixel-level
label noise. Existing noisy label methods, nevertheless, mainly aim at
image-level tasks, which can not capture the relationship between neighboring
labels in one image. Therefore, we propose a graph based label noise detection
and correction framework to deal with pixel-level noisy labels. In particular,
for the generated pixel-level noisy labels from weak supervisions by Class
Activation Map (CAM), we train a clean segmentation model with strong
supervisions to detect the clean labels from these noisy labels according to
the cross-entropy loss. Then, we adopt a superpixel-based graph to represent
the relations of spatial adjacency and semantic similarity between pixels in
one image. Finally we correct the noisy labels using a Graph Attention Network
(GAT) supervised by detected clean labels. We comprehensively conduct
experiments on PASCAL VOC 2012, PASCAL-Context and MS-COCO datasets. The
experimental results show that our proposed semi supervised method achieves the
state-of-the-art performances and even outperforms the fully-supervised models
on PASCAL VOC 2012 and MS-COCO datasets in some cases.
