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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 121–130 of 190 papers

TitleStatusHype
Discovering Latent Classes for Semi-Supervised Semantic Segmentation—0
DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery—0
Enhanced Soft Label for Semi-Supervised Semantic Segmentation—0
Feature-enhanced Adversarial Semi-supervised Semantic Segmentation Network for Pulmonary Embolism Annotation—0
Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation—0
Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation—0
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
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