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.

我们提出了一种新的半监督语义分割视角，通过对训练数据集中标记和无标记分布的分析，我们发现即使两个数据集采样自相同分布，标记和无标记数据集之间的分布差异也不能忽视。为了解决这个问题，我们在理论上进行了分析并从实验上证明了适当提高无标记数据的不确定性可以帮助减小分布差异，从而提高模型的推广能力。我们提出了两种策略，并设计了一种特别针对半监督语义分割的不确定性增强算法，基于这些理论进行了大量实验，结果证实了算法和策略的有效性。我们的插拔式不确定性增强器小巧高效，对于超参数具有鲁棒性，但可以显著提升性能。与当前流行的半监督语义分割方法在常用基准数据集Cityscapes和PASCAL VOC 2012上不同的训练设置下，我们的方法在实验中取得了最新性能。

通过增强未标记数据上的不确定性改进半监督语义分割