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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 21–30 of 190 papers

TitleStatusHype
Masked Image Modeling Boosting Semi-Supervised Semantic SegmentationCode0
Revisiting Network Perturbation for Semi-Supervised Semantic SegmentationCode0
UniMatch V2: Pushing the Limit of Semi-Supervised Semantic SegmentationCode3
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
Beyond Pixels: Semi-Supervised Semantic Segmentation with a Multi-scale Patch-based Multi-Label ClassifierCode1
SS-ADA: A Semi-Supervised Active Domain Adaptation Framework for Semantic SegmentationCode1
Revisiting and Maximizing Temporal Knowledge in Semi-supervised Semantic SegmentationCode1
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