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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 101–110 of 190 papers

TitleStatusHype
Multi-Level Label Correction by Distilling Proximate Patterns for Semi-supervised Semantic Segmentation—0
FishSegSSL: A Semi-Supervised Semantic Segmentation Framework for Fish-Eye ImagesCode0
Semi-Supervised Semantic Segmentation Based on Pseudo-Labels: A Survey—0
PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation—0
Floor Plan Image Segmentation Via Scribble-Based Semi-Weakly Supervised Learning: A Style and Category-Agnostic ApproachCode0
PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost DisturbancesCode0
Semi-Supervised Semantic Segmentation using Redesigned Self-Training for White Blood Cells—0
Semi-supervised Semantic Segmentation Meets Masked Modeling:Fine-grained Locality Learning Matters in Consistency Regularization—0
Semi-supervised Semantic Segmentation via Boosting Uncertainty on Unlabeled Data—0
DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery—0
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