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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
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation—0
Learning from Pixel-Level Label Noise: A New Perspective for Semi-Supervised Semantic Segmentation—0
Mask-based Data Augmentation for Semi-supervised Semantic Segmentation—0
C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing—0
Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image—0
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study—0
Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images—0
Guided Collaborative Training for Pixel-wise Semi-Supervised Learning—0
Semi-supervised Semantic Segmentation via Strong-weak Dual-branch Network—0
Learning High-Resolution Domain-Specific Representations with a GAN Generator—0
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