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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 121–130 of 190 papers

TitleStatusHype
SLRNet: Semi-Supervised Semantic Segmentation Via Label Reuse for Human Decomposition ImagesCode0
GuidedMix-Net: Semi-supervised Semantic Segmentation by Using Labeled Images as Reference—0
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation—0
Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation—0
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning—0
Perturbed and Strict Mean Teachers for Semi-supervised Semantic SegmentationCode1
Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations—0
Simpler Does It: Generating Semantic Labels with Objectness Guidance—0
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
Semi-Supervised Semantic Segmentation via Adaptive Equalization LearningCode1
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