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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 71–80 of 190 papers

TitleStatusHype
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR SegmentationCode1
Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline InvestigationCode1
Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory BankCode1
Learning from Future: A Novel Self-Training Framework for Semantic SegmentationCode1
Switching Temporary Teachers for Semi-Supervised Semantic SegmentationCode1
Improved Training for Self-Training by Confidence Assessments—0
IGL-DT: Iterative Global-Local Feature Learning with Dual-Teacher Semantic Segmentation Framework under Limited Annotation Scheme—0
Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image—0
Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Images—0
HierVL: Semi-Supervised Segmentation leveraging Hierarchical Vision-Language Synergy with Dynamic Text-Spatial Query Alignment—0
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