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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 71–80 of 190 papers

TitleStatusHype
DMT: Dynamic Mutual Training for Semi-Supervised LearningCode1
Semi-Supervised Semantic Segmentation with Cross-Consistency TrainingCode1
Semi-supervised semantic segmentation needs strong, varied perturbationsCode1
Adversarial Learning for Semi-Supervised Semantic SegmentationCode1
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultsCode1
SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation—0
DEARLi: Decoupled Enhancement of Recognition and Localization for Semi-supervised Panoptic SegmentationCode0
Leveraging Out-of-Distribution Unlabeled Images: Semi-Supervised Semantic Segmentation with an Open-Vocabulary ModelCode0
HierVL: Semi-Supervised Segmentation leveraging Hierarchical Vision-Language Synergy with Dynamic Text-Spatial Query Alignment—0
FARCLUSS: Fuzzy Adaptive Rebalancing and Contrastive Uncertainty Learning for Semi-Supervised Semantic SegmentationCode0
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