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We present ReCo, a contrastive learning framework designed at a regionallevel to assist learning in semantic segmentation. ReCo performssemi-supervised or supervised pixel-level contrastive learning on a sparse setof hard negative pixels, with minimal additional memory footprint. ReCo is easyto implement, being built on top of off-the-shelf segmentation networks, andconsistently improves performance in both semi-supervised and supervisedsemantic segmentation methods, achieving smoother segmentation boundaries andfaster convergence. The strongest effect is in semi-supervised learning withvery few labels. With ReCo, we achieve high-quality semantic segmentationmodels, requiring only 5 examples of each semantic class. Code is available athttps://github.com/lorenmt/reco.

Bootstrapping Semantic Segmentation with Regional Contrast