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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 51–60 of 190 papers

TitleStatusHype
COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud SegmentationCode1
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic SegmentationCode1
Semi-supervised semantic segmentation needs strong, varied perturbationsCode1
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
Beyond Pixels: Semi-Supervised Semantic Segmentation with a Multi-scale Patch-based Multi-Label ClassifierCode1
Conflict-Based Cross-View Consistency for Semi-Supervised Semantic SegmentationCode1
Hunting Sparsity: Density-Guided Contrastive Learning for Semi-Supervised Semantic SegmentationCode1
Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic SegmentationCode1
Missing Modality Robustness in Semi-Supervised Multi-Modal Semantic SegmentationCode1
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