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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 131–140 of 190 papers

TitleStatusHype
Colour augmentation for improved semi-supervised semantic segmentation—0
FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation DecodingCode1
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation—0
Robust Semantic Segmentation with Superpixel-MixCode1
Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline InvestigationCode1
Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic SegmentationCode1
Superpoint-guided Semi-supervised Semantic Segmentation of 3D Point Clouds—0
GuidedMix-Net: Learning to Improve Pseudo Masks Using Labeled Images as ReferenceCode0
Semi-supervised Semantic Segmentation with Directional Context-aware ConsistencyCode1
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