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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
Improving Semi-Supervised Semantic Segmentation with Sliced-Wasserstein Feature Alignment and Uniformity—0
Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical ImagingCode0
Masked Image Modeling Boosting Semi-Supervised Semantic SegmentationCode0
Revisiting Network Perturbation for Semi-Supervised Semantic SegmentationCode0
Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic SegmentationCode0
Exploiting Minority Pseudo-Labels for Semi-Supervised Semantic Segmentation in Autonomous Driving—0
Exploring Scene Affinity for Semi-Supervised LiDAR Semantic SegmentationCode0
Weighting Pseudo-Labels via High-Activation Feature Index Similarity and Object Detection for Semi-Supervised SegmentationCode0
IPixMatch: Boost Semi-supervised Semantic Segmentation with Inter-Pixel Relation—0
PV-S3: Advancing Automatic Photovoltaic Defect Detection using Semi-Supervised Semantic Segmentation of Electroluminescence ImagesCode0
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