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Poster

Bootstrapping Semantic Segmentation with Regional Contrast

Shikun Liu · Shuaifeng Zhi · Edward Johns · Andrew Davison

Virtual

Keywords: [ semantic segmentation ] [ contrastive learning ] [ semi-supervised learning ]


Abstract:

We present ReCo, a contrastive learning framework designed at a regional level to assist learning in semantic segmentation. ReCo performs 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, achieving more accurate 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 model, requiring only 5 examples of each semantic class.

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