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.

翻译：为了克服数据饥饿的挑战,我们提议了一个半监督的对比学习框架,以完成分类平衡语义分割的任务。首先,为了使模型以半监督的方式运作,我们提议了基于信任的对比学习,以明确实现实例歧视,并使低信任低质量特征与高信任对应方保持一致。此外,为了解决裂痕分割和道路部件提取中的阶级不平衡问题,我们提议了数据不平衡损失,以取代像素等级语义分割中传统的交叉酶损失。最后,我们还提议了一个有效的多阶段融合网络结构,以改善语义分割性表现。关于实际工业裂痕分割和路段分割的广泛实验显示了拟议框架的优越性。我们提出的方法可以提供令人满意的分解结果,甚至只有3.5%的标签数据。