  Convolutional neural networks can achieve remarkable performance in semantic
segmentation tasks. However, such neural network approaches heavily rely on
costly pixel-level annotation. Semi-supervised learning is a promising
resolution to tackle this issue, but its performance still far falls behind the
fully supervised counterpart. This work proposes a cross-teacher training
framework with three modules that significantly improves traditional
semi-supervised learning approaches. The core is a cross-teacher module, which
could simultaneously reduce the coupling among peer networks and the error
accumulation between teacher and student networks. In addition, we propose two
complementary contrastive learning modules. The high-level module can transfer
high-quality knowledge from labeled data to unlabeled ones and promote
separation between classes in feature space. The low-level module can encourage
low-quality features learning from the high-quality features among peer
networks. In experiments, the cross-teacher module significantly improves the
performance of traditional student-teacher approaches, and our framework
outperforms stateof-the-art methods on benchmark datasets. Our source code of
CTT will be released.
