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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 161–170 of 190 papers

TitleStatusHype
S4-Net: Geometry-Consistent Semi-Supervised Semantic Segmentation—0
SAMST: A Transformer framework based on SAM pseudo label filtering for remote sensing semi-supervised semantic segmentation—0
Superpoint-guided Semi-supervised Semantic Segmentation of 3D Point Clouds—0
Semi-Supervised Building Footprint Generation with Feature and Output Consistency Training—0
Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey—0
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study—0
Semi-supervised Semantic Segmentation Meets Masked Modeling:Fine-grained Locality Learning Matters in Consistency Regularization—0
Semi-Supervised Semantic Segmentation Methods for UW-OCTA Diabetic Retinopathy Grade Assessment—0
Semi-supervised semantic segmentation needs strong, high-dimensional perturbations—0
Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images—0
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