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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 91–100 of 190 papers

TitleStatusHype
FixMatchSeg: Fixing FixMatch for Semi-Supervised Semantic Segmentation—0
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning—0
GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference—0
HierVL: Semi-Supervised Segmentation leveraging Hierarchical Vision-Language Synergy with Dynamic Text-Spatial Query Alignment—0
Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image—0
IGL-DT: Iterative Global-Local Feature Learning with Dual-Teacher Semantic Segmentation Framework under Limited Annotation Scheme—0
Improved Training for Self-Training by Confidence Assessments—0
Improving Semi-Supervised Semantic Segmentation with Sliced-Wasserstein Feature Alignment and Uniformity—0
Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays—0
IPixMatch: Boost Semi-supervised Semantic Segmentation with Inter-Pixel Relation—0
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