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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 131–140 of 190 papers

TitleStatusHype
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
Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations—0
Semi-Supervised Semantic Segmentation of Cell Nuclei via Diffusion-based Large-Scale Pre-Training and Collaborative Learning—0
Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups—0
Semi Supervised Semantic Segmentation Using Generative Adversarial Network—0
Semi-supervised Semantic Segmentation using Auxiliary Network—0
Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells—0
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