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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
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization—0
Mapping Temporary Slums from Satellite Imagery using a Semi-Supervised Approach—0
Feature-enhanced Adversarial Semi-supervised Semantic Segmentation Network for Pulmonary Embolism Annotation—0
Semi-supervised Semantic Segmentation with Error Localization NetworkCode1
Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools SegmentationCode3
Translation Consistent Semi-supervised Segmentation for 3D Medical ImagesCode1
Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic SegmentationCode1
Unbiased Subclass Regularization for Semi-Supervised Semantic SegmentationCode1
Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-LabelsCode2
Adversarial Dual-Student with Differentiable Spatial Warping for Semi-Supervised Semantic SegmentationCode1
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