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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 41–50 of 190 papers

TitleStatusHype
PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost DisturbancesCode0
Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells—0
RankMatch: Exploring the Better Consistency Regularization for Semi-supervised Semantic SegmentationCode1
Training Vision Transformers for Semi-Supervised Semantic SegmentationCode1
Semi-supervised Semantic Segmentation Meets Masked Modeling:Fine-grained Locality Learning Matters in Consistency Regularization—0
Semi-supervised Semantic Segmentation via Boosting Uncertainty on Unlabeled Data—0
SemiVL: Semi-Supervised Semantic Segmentation with Vision-Language GuidanceCode1
DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery—0
Triple-View Knowledge Distillation for Semi-Supervised Semantic Segmentation—0
Switching Temporary Teachers for Semi-Supervised Semantic SegmentationCode1
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