Semi-supervised Semantic Segmentation via Boosting Uncertainty on
  Unlabeled Data
Daoan Zhang Yunhao Luo Jianguo Zhang
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.