  We present ReCo, a contrastive learning framework designed at a regional
level to assist learning in semantic segmentation. ReCo performs
semi-supervised or supervised pixel-level contrastive learning on a sparse set
of hard negative pixels, with minimal additional memory footprint. ReCo is easy
to implement, being built on top of off-the-shelf segmentation networks, and
consistently improves performance in both semi-supervised and supervised
semantic segmentation methods, achieving smoother segmentation boundaries and
faster convergence. The strongest effect is in semi-supervised learning with
very few labels. With ReCo, we achieve high-quality semantic segmentation
models, requiring only 5 examples of each semantic class. Code is available at
https://github.com/lorenmt/reco.
