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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 71–80 of 190 papers

TitleStatusHype
Iterative Loop Method Combining Active and Semi-Supervised Learning for Domain Adaptive Semantic SegmentationCode1
Consistency Regularisation in Varying Contexts and Feature Perturbations for Semi-Supervised Semantic Segmentation of Histology Images—0
Semi-Supervised Semantic Segmentation via Gentle Teaching AssistantCode1
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image SegmentationCode1
Locating Noise is Halfway Denoising for Semi-Supervised Segmentation—0
CFCG: Semi-Supervised Semantic Segmentation via Cross-Fusion and Contour Guidance Supervision—0
Enhanced Soft Label for Semi-Supervised Semantic Segmentation—0
Semi-Supervised Semantic Segmentation under Label Noise via Diverse Learning Groups—0
Large-Scale Land Cover Mapping with Fine-Grained Classes via Class-Aware Semi-Supervised Semantic Segmentation—0
XNet: Wavelet-Based Low and High Frequency Fusion Networks for Fully- and Semi-Supervised Semantic Segmentation of Biomedical ImagesCode2
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