We bring a new perspective to semi-supervised semantic segmentation by providing an analysis on the labeled and unlabeled distributions in training datasets. We first figure out that the distribution gap between labeled and unlabeled datasets cannot be ignored, even though the two datasets are sampled from the same distribution. To address this issue, we theoretically analyze and experimentally prove that appropriately boosting uncertainty on unlabeled data can help minimize the distribution gap, which benefits the generalization of the model. We propose two strategies and design an uncertainty booster algorithm, specially for semi-supervised semantic segmentation. Extensive experiments are carried out based on these theories, and the results confirm the efficacy of the algorithm and strategies. Our plug-and-play uncertainty booster is tiny, efficient, and robust to hyperparameters but can significantly promote performance. Our approach achieves state-of-the-art performance in our experiments compared to the current semi-supervised semantic segmentation methods on the popular benchmarks: Cityscapes and PASCAL VOC 2012 with different train settings.

翻译：我们从训练数据集中标注与未标注数据分布的角度，为半监督语义分割提供了新的视角。首先发现，即使标注数据集与未标注数据集采样自同一分布，两者之间的分布差异也不可忽视。为解决该问题，我们从理论角度分析并通过实验证明，适当提升未标注数据的不确定性有助于缩小分布差异，从而提升模型的泛化能力。我们提出了两种策略，并设计了一种专门针对半监督语义分割的不确定性提升算法。基于这些理论开展了大量实验，结果验证了算法及策略的有效性。我们的即插即用不确定性提升模块体积小、效率高、对超参数鲁棒，且能显著提升性能。在主流基准数据集Cityscapes与PASCAL VOC 2012的不同训练设置下，与当前半监督语义分割方法相比，我们的方法在实验中达到了最先进的性能水平。