Semi-supervised semantic segmentation (SSS) is an important task that
utilizes both labeled and unlabeled data to reduce expenses on labeling
training examples. However, the effectiveness of SSS algorithms is limited by
the difficulty of fully exploiting the potential of unlabeled data. To address
this, we propose a dual-level Siamese structure network (DSSN) for pixel-wise
contrastive learning. By aligning positive pairs with a pixel-wise contrastive
loss using strong augmented views in both low-level image space and high-level
feature space, the proposed DSSN is designed to maximize the utilization of
available unlabeled data. Additionally, we introduce a novel class-aware
pseudo-label selection strategy for weak-to-strong supervision, which addresses
the limitations of most existing methods that do not perform selection or apply
a predefined threshold for all classes. Specifically, our strategy selects the
top high-confidence prediction of the weak view for each class to generate
pseudo labels that supervise the strong augmented views. This strategy is
capable of taking into account the class imbalance and improving the
performance of long-tailed classes. Our proposed method achieves
state-of-the-art results on two datasets, PASCAL VOC 2012 and Cityscapes,
outperforming other SSS algorithms by a significant margin.

我们提出了一种双层 Siamese 结构网络（DSSN）进行像素级对比学习，通过在低级图像空间和高级特征空间中利用强增强视图对齐正样本对，最大化利用可用的无标签数据，并引入了一种新颖的面向类别感知的伪标签选择策略以改善弱到强监督的性能，并在 PASCAL VOC 2012 和 Cityscapes 两个数据集上取得了显著优于其他 SSS 算法的最新成果。