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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 31–40 of 190 papers

TitleStatusHype
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic SegmentationCode1
Boosting Semi-Supervised Semantic Segmentation with Probabilistic RepresentationsCode1
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image SegmentationCode1
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR SegmentationCode1
Bootstrapping Semantic Segmentation with Regional ContrastCode1
Improving Semi-Supervised Semantic Segmentation with Dual-Level Siamese Structure NetworkCode1
Inconsistency Masks: Removing the Uncertainty from Input-Pseudo-Label PairsCode1
FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation DecodingCode1
CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic SegmentationCode1
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