  This work presents a novel approach for semi-supervised semantic
segmentation, i.e., per-pixel classification problem assuming that only a small
set of the available data is labeled. We propose a novel representation
learning module based on contrastive learning. This module 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 feature vectors from labeled data. These
features are selected based on their quality and relevance for the contrastive
learning. 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.
