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 51–60 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
Show:102550
← PrevPage 6 of 19Next →

No leaderboard results yet.