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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 151–160 of 190 papers

TitleStatusHype
Mask-based Data Augmentation for Semi-supervised Semantic Segmentation—0
C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing—0
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth EstimationCode1
Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image—0
A Three-Stage Self-Training Framework for Semi-Supervised Semantic SegmentationCode1
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR SegmentationCode1
PseudoSeg: Designing Pseudo Labels for Semantic SegmentationCode1
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study—0
Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images—0
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning—0
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