OpenCodePapers

semi-supervised-semantic-segmentation-on-24

Semantic SegmentationSemi-Supervised Semantic Segmentation
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PaperCodemIoU (0.5% Labels)mIoU (1% Labels)mIoU (2% Labels)mIoU (5% Labels)mIoU (10% Labels)mIoU (20% Labels)mIoU (50% Labels)mIOU (1% Test set)ModelNameReleaseDate
Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation✓ Link52.261.162.962.863.164.164.3PLE (Voxel)2024-10-09
Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation✓ Link47.359.261.7LaserMix (Voxel)2024-10-09
Learning from Spatio-temporal Correlation for Semi-Supervised LiDAR Semantic Segmentation✓ Link46.251.554.358.1PLE (CENet, Range view)2024-10-09
360$^\circ$ from a Single Camera: A Few-Shot Approach for LiDAR Segmentation59.562.464.266.157.7360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation (All)2023-09-12
What Can be Seen is What You Get: Structure Aware Point Cloud Augmentation50.964.064.9SAPCA (Cylinder3D)2022-06-20
LaserMix for Semi-Supervised LiDAR Semantic Segmentation✓ Link50.660.061.962.3LaserMix (Voxel)2022-06-30
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results✓ Link45.457.159.260.0MeanTeacher (Voxel)2017-03-06
LaserMix for Semi-Supervised LiDAR Semantic Segmentation✓ Link43.458.859.461.4LaserMix (Range View)2022-06-30
Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training✓ Link39.953.456.156.9CBST (Range View)2018-09-01
Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results✓ Link37.553.156.157.4MeanTeacher (Range View)2017-03-06
Semi-supervised semantic segmentation needs strong, varied perturbations✓ Link37.454.356.657.6CutMix-Seg (Range View)2019-06-05
Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision✓ Link36.552.356.357.4CPS (Range View)2021-06-02