Tremendous breakthroughs have been developed in Semi-Supervised Semantic
Segmentation (S4) through contrastive learning. However, due to limited
annotations, the guidance on unlabeled images is generated by the model itself,
which inevitably exists noise and disturbs the unsupervised training process.
To address this issue, we propose a robust contrastive-based S4 framework,
termed the Probabilistic Representation Contrastive Learning (PRCL) framework
to enhance the robustness of the unsupervised training process. We model the
pixel-wise representation as Probabilistic Representations (PR) via
multivariate Gaussian distribution and tune the contribution of the ambiguous
representations to tolerate the risk of inaccurate guidance in contrastive
learning. Furthermore, we introduce Global Distribution Prototypes (GDP) by
gathering all PRs throughout the whole training process. Since the GDP contains
the information of all representations with the same class, it is robust from
the instant noise in representations and bears the intra-class variance of
representations. In addition, we generate Virtual Negatives (VNs) based on GDP
to involve the contrastive learning process. Extensive experiments on two
public benchmarks demonstrate the superiority of our PRCL framework.

通过对半监督语义分割进行对比学习取得了巨大突破，但是由于有限的注释，模型自身生成的无标签图像上的引导 inevitably 存在噪音，扰乱了无监督训练过程。为了解决这个问题，我们提出了一个强大的基于对比的 S4 框架，称为概率表示对比学习 (PRCL) 框架来增强无监督训练过程的鲁棒性。我们将逐像素表示建模为多元高斯分布的概率表示 (PR), 并通过调整模糊表示的贡献度来容忍对比学习中的不准确引导的风险。此外，我们通过收集整个训练过程中所有 PR 生成全局分布原型 (GDP)。由于 GDP 包含相同类别的所有表示的信息，它在表示中即使存在噪声也是鲁棒的，并且承载着表示的类内差异。此外，我们基于 GDP 生成虚拟负样本 (VNs) 来参与对比学习过程。在两个公开基准测试上进行的大量实验证明了我们 PRCL 框架的优越性。