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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 31–40 of 190 papers

TitleStatusHype
Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image SegmentationCode1
CauSSL: Causality-inspired Semi-supervised Learning for Medical Image SegmentationCode1
Hunting Sparsity: Density-Guided Contrastive Learning for Semi-Supervised Semantic SegmentationCode1
Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic SegmentationCode1
Conservative-Progressive Collaborative Learning for Semi-supervised Semantic SegmentationCode1
Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic SegmentationCode1
Boosting Semi-Supervised Semantic Segmentation with Probabilistic RepresentationsCode1
Semi-supervised Semantic Segmentation with Prototype-based Consistency RegularizationCode1
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud SegmentationCode1
Learning from Future: A Novel Self-Training Framework for Semantic SegmentationCode1
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