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Semi-Supervised Semantic Segmentation

Models that are trained with a small number of labeled examples and a large number of unlabeled examples and whose aim is to learn to segment an image (i.e. assign a class to every pixel).

Papers

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Showing 151–160 of 190 papers

TitleStatusHype
Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation—0
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning—0
Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations—0
Simpler Does It: Generating Semantic Labels with Objectness Guidance—0
Colour augmentation for improved semi-supervised semantic segmentation—0
Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation—0
Superpoint-guided Semi-supervised Semantic Segmentation of 3D Point Clouds—0
GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as ReferenceCode0
Revisiting consistency for semi-supervised semantic segmentationCode0
Robust Mutual Learning for Semi-supervised Semantic Segmentation—0
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