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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 111–120 of 190 papers

TitleStatusHype
Multi-Level Label Correction by Distilling Proximate Patterns for Semi-supervised Semantic Segmentation—0
Multi-View Correlation Consistency for Semi-Supervised Semantic Segmentation—0
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles—0
n-CPS: Generalising Cross Pseudo Supervision to n Networks for Semi-Supervised Semantic Segmentation—0
Fuzzy Positive Learning for Semi-supervised Semantic Segmentation—0
Pixel Contrastive-Consistent Semi-Supervised Semantic Segmentation—0
PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation—0
Progressive Learning with Cross-Window Consistency for Semi-Supervised Semantic Segmentation—0
Reference-guided Pseudo-Label Generation for Medical Semantic Segmentation—0
Region-level Contrastive and Consistency Learning for Semi-Supervised Semantic Segmentation—0
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