PRCL: Probabilistic Representation Contrastive Learning for
  Semi-Supervised Semantic Segmentation
Haoyu Xie Changqi Wang Jian Zhao Yang Liu Jun Dan Chong Fu Baigui Sun
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.