SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–75 of 190 papers

TitleStatusHype
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
Semi-Supervised Semantic Segmentation via Adaptive Equalization LearningCode1
FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation DecodingCode1
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
Robust Semantic Segmentation with Superpixel-MixCode1
Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline InvestigationCode1
Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic SegmentationCode1
Semi-supervised Semantic Segmentation with Directional Context-aware ConsistencyCode1
ST++: Make Self-training Work Better for Semi-supervised Semantic SegmentationCode1
Semi-Supervised Semantic Segmentation with Cross Pseudo SupervisionCode1
Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory BankCode1
A Simple Baseline for Semi-supervised Semantic Segmentation with Strong Data AugmentationCode1
Bootstrapping Semantic Segmentation with Regional ContrastCode1
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic SegmentationCode1
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth EstimationCode1
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
ClassMix: Segmentation-Based Data Augmentation for Semi-Supervised LearningCode1
Part-aware Prototype Network for Few-shot Semantic SegmentationCode1
DMT: Dynamic Mutual Training for Semi-Supervised LearningCode1
Semi-Supervised Semantic Segmentation with Cross-Consistency TrainingCode1
Semi-supervised semantic segmentation needs strong, varied perturbationsCode1
Adversarial Learning for Semi-Supervised Semantic SegmentationCode1
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning resultsCode1
Show:102550
← PrevPage 3 of 8Next →

No leaderboard results yet.