Semi-supervised semantic segmentation has recently gained increasing research interest as it can reduce the requirement for large-scale fully-annotated training data by effectively exploiting large amounts of unlabelled data. The current methods often suffer from the confirmation bias from the pseudo-labelling process, which can be alleviated by the co-training framework. The current co-training-based semi-supervised semantic segmentation methods rely on hand-crafted perturbations to prevent the different sub-nets from collapsing into each other, but these artificial perturbations cannot lead to the optimal solution. In this work, we propose a new conflict-based cross-view consistency (CCVC) method based on a two-branch co-training framework for semi-supervised semantic segmentation. Our work aims at enforcing the two sub-nets to learn informative features from irrelevant views. In particular, we first propose a new cross-view consistency (CVC) strategy that encourages the two sub-nets to learn distinct features from the same input by introducing a feature discrepancy loss, while these distinct features are expected to generate consistent prediction scores of the input. The CVC strategy helps to prevent the two sub-nets from stepping into the collapse. In addition, we further propose a conflict-based pseudo-labelling (CPL) method to guarantee the model will learn more useful information from conflicting predictions, which will lead to a stable training process. We validate our new semi-supervised semantic segmentation approach on the widely used benchmark datasets PASCAL VOC 2012 and Cityscapes, where our method achieves new state-of-the-art performance.
翻译:半监督的语义分割最近引起了越来越多的研究兴趣,因为它能够通过有效利用大量未贴标签的数据,减少对大规模全面附加说明的培训数据的需求。 目前的方法通常会因假标签过程的确认偏差而受到影响,而这种伪标签过程可以通过共同培训框架来缓解。目前以共同培训为基础的半监督语义分割方法依靠手动制作的交叉扰动策略,以防止不同的子网互爆,但这些人为的扰动无法导致最佳解决方案。在这项工作中,我们建议采用基于冲突的跨视图一致性(CCVC)新方法。在基于两处的双处共同培训框架的基础上,采用新的基于半监督的语义分割法。我们的工作旨在执行两个子网,从无关的观点中学习信息特性。特别是,我们首先提出一个新的跨视图一致性(CVC)战略,通过引入功能差异损失来鼓励两个子网络的新输入的特性。同时,这些截然不同的功能将使得基于冲突的跨视图的跨视图的跨视图(CC)方法能够产生稳定的计算方法。</s>