Pseudo supervision is regarded as the core idea in semi-supervised learning for semantic segmentation, and there is always a tradeoff between utilizing only the high-quality pseudo labels and leveraging all the pseudo labels. Addressing that, we propose a novel learning approach, called Conservative-Progressive Collaborative Learning (CPCL), among which two predictive networks are trained in parallel, and the pseudo supervision is implemented based on both the agreement and disagreement of the two predictions. One network seeks common ground via intersection supervision and is supervised by the high-quality labels to ensure a more reliable supervision, while the other network reserves differences via union supervision and is supervised by all the pseudo labels to keep exploring with curiosity. Thus, the collaboration of conservative evolution and progressive exploration can be achieved. To reduce the influences of the suspicious pseudo labels, the loss is dynamic re-weighted according to the prediction confidence. Extensive experiments demonstrate that CPCL achieves state-of-the-art performance for semi-supervised semantic segmentation.

翻译：伪监督被视为半监督语义分割中的核心思想，且始终存在一个权衡：是仅利用高质量伪标签，还是充分利用所有伪标签。针对这一问题，我们提出一种新颖的学习方法，称为保守-渐进协同学习（CPCL），其中两个预测网络并行训练，伪监督基于两种预测的一致性和差异性共同实现。一个网络通过交集监督寻求共同点，并由高质量标签监督以确保更可靠的监督；另一个网络通过并集监督保留差异，并由所有伪标签监督以保持探索的好奇心。由此，保守演化与渐进探索的协同得以实现。为了减少可疑伪标签的影响，损失函数根据预测置信度进行动态重新加权。大量实验表明，CPCL在半监督语义分割中达到了最先进的性能。