  To overcome the data-hungry challenge, we have proposed a semi-supervised
contrastive learning framework for the task of class-imbalanced semantic
segmentation. First and foremost, to make the model operate in a
semi-supervised manner, we proposed the confidence-level-based contrastive
learning to achieve instance discrimination in an explicit manner, and make the
low-confidence low-quality features align with the high-confidence
counterparts. Moreover, to tackle the problem of class imbalance in crack
segmentation and road components extraction, we proposed the data imbalance
loss to replace the traditional cross entropy loss in pixel-level semantic
segmentation. Finally, we have also proposed an effective multi-stage fusion
network architecture to improve semantic segmentation performance. Extensive
experiments on the real industrial crack segmentation and the road segmentation
demonstrate the superior effectiveness of the proposed framework. Our proposed
method can provide satisfactory segmentation results with even merely 3.5%
labeled data.
