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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 151–190 of 190 papers

TitleStatusHype
Mask-based Data Augmentation for Semi-supervised Semantic Segmentation—0
C3-SemiSeg: Contrastive Semi-Supervised Segmentation via Cross-Set Learning and Dynamic Class-Balancing—0
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth EstimationCode1
Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image—0
A Three-Stage Self-Training Framework for Semi-Supervised Semantic SegmentationCode1
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR SegmentationCode1
PseudoSeg: Designing Pseudo Labels for Semantic SegmentationCode1
Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study—0
Semi-supervised Semantic Segmentation of Prostate and Organs-at-Risk on 3D Pelvic CT Images—0
Guided Collaborative Training for Pixel-wise Semi-Supervised LearningCode0
Semi-supervised Semantic Segmentation via Strong-weak Dual-branch Network—0
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised LearningCode1
Part-aware Prototype Network for Few-shot Semantic SegmentationCode1
Learning High-Resolution Domain-Specific Representations with a GAN Generator—0
DMT: Dynamic Mutual Training for Semi-Supervised LearningCode1
Semi-Supervised Semantic Segmentation with Cross-Consistency TrainingCode1
Structured Consistency Loss for semi-supervised semantic segmentation—0
Discovering Latent Classes for Semi-Supervised Semantic Segmentation—0
Saliency Guided Self-attention Network for Weakly and Semi-supervised Semantic SegmentationCode0
Semi-supervised Semantic Segmentation using Auxiliary Network—0
Semi-supervised semantic segmentation needs strong, high-dimensional perturbations—0
Semi-Supervised Semantic Segmentation with High- and Low-level ConsistencyCode0
Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation—0
Semi-supervised semantic segmentation needs strong, varied perturbationsCode1
Curriculum semi-supervised segmentationCode0
S4-Net: Geometry-Consistent Semi-Supervised Semantic Segmentation—0
Fast Online Object Tracking and Segmentation: A Unifying ApproachCode2
Universal Semi-Supervised Semantic SegmentationCode0
Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays—0
Few-shot 3D Multi-modal Medical Image Segmentation using Generative Adversarial LearningCode0
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-TrainingCode0
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi-Supervised Semantic Segmentation—0
Revisiting Dilated Convolution: A Simple Approach for Weakly- and Semi- Supervised Semantic Segmentation—0
Adversarial Learning for Semi-Supervised Semantic SegmentationCode1
Transferable Semi-supervised Semantic Segmentation—0
Semi Supervised Semantic Segmentation Using Generative Adversarial Network—0
Improved Training for Self-Training by Confidence Assessments—0
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultsCode1
Decoupled Deep Neural Network for Semi-supervised Semantic SegmentationCode0
Weakly- and Semi-Supervised Learning of a DCNN for Semantic Image SegmentationCode0
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