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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 101–110 of 190 papers

TitleStatusHype
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic SegmentationCode2
Multi-View Correlation Consistency for Semi-Supervised Semantic Segmentation—0
Triple-View Feature Learning for Medical Image SegmentationCode1
Learning Pseudo Labels for Semi-and-Weakly Supervised Semantic SegmentationCode0
FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation—0
LaserMix for Semi-Supervised LiDAR Semantic SegmentationCode2
What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation—0
UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic SegmentationCode1
Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training—0
Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation—0
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