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 31–40 of 190 papers

TitleStatusHype
Improving Semi-Supervised and Domain-Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic SegmentationCode1
Boosting Semi-Supervised Semantic Segmentation with Probabilistic RepresentationsCode1
Co-training with High-Confidence Pseudo Labels for Semi-supervised Medical Image SegmentationCode1
Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR SegmentationCode1
Bootstrapping Semantic Segmentation with Regional ContrastCode1
FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation DecodingCode1
Improving Semi-Supervised Semantic Segmentation with Dual-Level Siamese Structure NetworkCode1
CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic SegmentationCode1
Semi-supervised semantic segmentation needs strong, varied perturbationsCode1
Show:102550
← PrevPage 4 of 19Next →

No leaderboard results yet.