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Apr 9, 2021 · We present ReCo, a contrastive learning framework designed at a regional level to assist learning in semantic segmentation.
Jan 28, 2022 · This paper proposes ReCo, a regional contrastive learning method for semi-supervised semantic segmentation. The query and key pixel sampling ...
We present ReCo, a contrastive learning framework designed at a regional level to assist learning in semantic segmentation. ReCo performs pixel-level ...
Annotation and quality control required more than 90 minutes on average for a single image in. Cityscapes dataset. Page 3. SEMI-SUPERVISED SEGMENTATION. • Case ...
This repository contains the source code of ReCo and baselines from the paper, Bootstrapping Semantic Segmentation with Regional Contrast, introduced by Shikun ...
ReCo performs semi-supervised or supervised pixel-level contrastive learning on a sparse set of hard negative pixels, with minimal additional memory ...
We've presented ReCo, a contrastive learning framework designed at a regional level to assist learning in semantic segmentation.
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Explore all code implementations available for Bootstrapping Semantic Segmentation with Regional Contrast.
We present ReCo, a contrastive learning framework designed at a regional level to assist learning in semantic segmentation. 2.
During the training, we introduce anatomical contrast by actively sampling a sparse set of hard negative pixels, which can generate smoother segmentation ...