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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 126–150 of 190 papers

TitleStatusHype
Perturbed and Strict Mean Teachers for Semi-supervised Semantic SegmentationCode1
Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations—0
Simpler Does It: Generating Semantic Labels with Objectness Guidance—0
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
Semi-Supervised Semantic Segmentation via Adaptive Equalization LearningCode1
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
Revisiting consistency for semi-supervised semantic segmentationCode0
ST++: Make Self-training Work Better for Semi-supervised Semantic SegmentationCode1
Semi-Supervised Semantic Segmentation with Cross Pseudo SupervisionCode1
Robust Mutual Learning for Semi-supervised Semantic Segmentation—0
Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory BankCode1
A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data AugmentationCode1
Bootstrapping Semantic Segmentation with Regional ContrastCode1
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation—0
Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation—0
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
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