Semi-Supervised Semantic Segmentation (S4) aims to train a segmentation model
with limited labeled images and a substantial volume of unlabeled images. To
improve the robustness of representations, powerful methods introduce a
pixel-wise contrastive learning approach in latent space (i.e., representation
space) that aggregates the representations to their prototypes in a fully
supervised manner. However, previous contrastive-based S4 methods merely rely
on the supervision from the model's output (logits) in logit space during
unlabeled training. In contrast, we utilize the outputs in both logit space and
representation space to obtain supervision in a collaborative way. The
supervision from two spaces plays two roles: 1) reduces the risk of
over-fitting to incorrect semantic information in logits with the help of
representations; 2) enhances the knowledge exchange between the two spaces.
Furthermore, unlike previous approaches, we use the similarity between
representations and prototypes as a new indicator to tilt training those
under-performing representations and achieve a more efficient contrastive
learning process. Results on two public benchmarks demonstrate the competitive
performance of our method compared with state-of-the-art methods.

半监督语义分割采用有限标记图像和大量未标记图像训练分割模型。使用像素级对比学习方法在潜空间中将表示聚合到原型，以获取来自逻辑空间和表示空间的协同监督，在减少过拟合风险和增强两个空间之间知识交流的基础上，通过表示与原型之间的相似性作为倾斜训练指标，实现更高效的对比学习过程。与现有方法相比，我们的方法在两个公共基准数据集上展现出有竞争力的性能。