Bootstrapping Semantic Segmentation with Regional Contrast

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... (read more)

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METHOD TYPE
ReCo
Self-Supervised Learning