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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 81–90 of 190 papers

TitleStatusHype
Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentation—0
Zero-Shot Pseudo Labels Generation Using SAM and CLIP for Semi-Supervised Semantic Segmentation—0
Feedback-Driven Pseudo-Label Reliability Assessment: Redefining Thresholding for Semi-Supervised Semantic Segmentation—0
IGL-DT: Iterative Global-Local Feature Learning with Dual-Teacher Semantic Segmentation Framework under Limited Annotation Scheme—0
BoundMatch: Boundary detection applied to semi-supervised segmentation for urban-driving scenes—0
Semi-supervised Semantic Segmentation with Multi-Constraint Consistency LearningCode0
Knowledge Consultation for Semi-Supervised Semantic Segmentation—0
Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic SegmentationCode0
Confidence-Weighted Boundary-Aware Learning for Semi-Supervised Semantic SegmentationCode0
Uncertainty and Energy based Loss Guided Semi-Supervised Semantic Segmentation—0
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