  This work presents a novel approach for semi-supervised semantic
segmentation. The key element of this approach is our contrastive learning
module that enforces the segmentation network to yield similar pixel-level
feature representations for same-class samples across the whole dataset. To
achieve this, we maintain a memory bank continuously updated with relevant and
high-quality feature vectors from labeled data. In an end-to-end training, the
features from both labeled and unlabeled data are optimized to be similar to
same-class samples from the memory bank. Our approach outperforms the current
state-of-the-art for semi-supervised semantic segmentation and semi-supervised
domain adaptation on well-known public benchmarks, with larger improvements on
the most challenging scenarios, i.e., less available labeled data.
https://github.com/Shathe/SemiSeg-Contrastive
