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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 91–100 of 190 papers

TitleStatusHype
Pseudo-Label Noise Suppression Techniques for Semi-Supervised Semantic SegmentationCode0
Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasksCode0
Fuzzy Positive Learning for Semi-supervised Semantic Segmentation—0
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
Transformer-CNN Cohort: Semi-supervised Semantic Segmentation by the Best of Both Students—0
Semi-Supervised Semantic Segmentation with Cross Teacher TrainingCode0
Semi-supervised Semantic Segmentation with Mutual Knowledge DistillationCode1
Multi-Granularity Distillation Scheme Towards Lightweight Semi-Supervised Semantic SegmentationCode1
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