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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 11–20 of 190 papers

TitleStatusHype
BoundMatch: Boundary detection applied to semi-supervised segmentation for urban-driving scenes—0
Semi-supervised Semantic Segmentation with Multi-Constraint Consistency LearningCode0
Knowledge Consultation for Semi-Supervised Semantic Segmentation—0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic SegmentationCode0
Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student AttentionCode1
Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation—0
Improving Semi-Supervised Semantic Segmentation with Sliced-Wasserstein Feature Alignment and Uniformity—0
Uncertainty-Participation Context Consistency Learning for Semi-supervised Semantic SegmentationCode1
Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical ImagingCode0
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