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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 171–180 of 190 papers

TitleStatusHype
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
Semi-supervised Semantic Segmentation via Strong-weak Dual-branch Network—0
Semi-supervised Semantic Segmentation via Boosting Uncertainty on Unlabeled Data—0
Exploiting Minority Pseudo-Labels for Semi-Supervised Semantic Segmentation in Autonomous Driving—0
Simpler Does It: Generating Semantic Labels with Objectness Guidance—0
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