Semi-supervised semantic segmentation (SSS) has recently gained increasing
research interest as it can reduce the requirement for large-scale
fully-annotated training 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 SSS 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 which 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
CCVC approach on the SSS benchmark datasets where our method achieves new
state-of-the-art performance. Our code is available at
this https URL

本文提出了一种新的基于双分支协同训练框架的基于冲突的跨视图一致性方法 (CCVC)，该方法使用特征差距损失鼓励两个子网络从相同的输入学习不同的特征，并使用冲突的伪标签方法来保证模型可以从冲突预测中学习更多有用的信息，从而在半监督语义分割 (Semantic Segmentation) 任务中取得了最新的最优性能.