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Models/CutMix-Seg (Range View)

CutMix-Seg (Range View)

Reported on 24 benchmarks across 2 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical12 results

Semantic SegmentationonScribbleKITTI
mIoU (10% Labels)· 2019-06-05
50.7
best: 54.4 (LaserMix (Range View))
SOTA
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
Semantic SegmentationonScribbleKITTI
mIoU (20% Labels)· 2019-06-05
52.9
best: 55.6 (LaserMix (Range View))
SOTA
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
Semantic SegmentationonScribbleKITTI
mIoU (50% Labels)· 2019-06-05
54.3
best: 58.7 (LaserMix (Range View))
SOTA
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
Semantic SegmentationonScribbleKITTI
mIoU (1% Labels)· 2019-06-05
36.7
best: 44.2 (LaserMix (Voxel))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
Semantic SegmentationonSemanticKITTI
mIoU (1% Labels)· 2019-06-05
37.4
best: 61.1 (PLE (Voxel))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
Semantic SegmentationonSemanticKITTI
mIoU (10% Labels)· 2019-06-05
54.3
best: 64 (SAPCA (Cylinder3D))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
Semantic SegmentationonSemanticKITTI
mIoU (20% Labels)· 2019-06-05
56.6
best: 64.2 (360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation (All))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
Semantic SegmentationonSemanticKITTI
mIoU (50% Labels)· 2019-06-05
57.6
best: 66.1 (360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation (All))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
Semantic SegmentationonnuScenes
mIoU (1% Labels)· 2019-06-05
43.8
best: 62.9 (PLE (Voxel))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
Semantic SegmentationonnuScenes
mIoU (10% Labels)· 2019-06-05
63.9
best: 74.3 (PLE (Voxel))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
Semantic SegmentationonnuScenes
mIoU (20% Labels)· 2019-06-05
64.8
best: 76 (PLE (Voxel))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
Semantic SegmentationonnuScenes
mIoU (50% Labels)· 2019-06-05
69.8
best: 76.1 (PLE (Voxel))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916

Audio12 results

10-shot image generationonScribbleKITTI
mIoU (10% Labels)· 2019-06-05
50.7
best: 54.4 (LaserMix (Range View))
SOTA
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
10-shot image generationonScribbleKITTI
mIoU (20% Labels)· 2019-06-05
52.9
best: 55.6 (LaserMix (Range View))
SOTA
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
10-shot image generationonScribbleKITTI
mIoU (50% Labels)· 2019-06-05
54.3
best: 58.7 (LaserMix (Range View))
SOTA
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
10-shot image generationonScribbleKITTI
mIoU (1% Labels)· 2019-06-05
36.7
best: 44.2 (LaserMix (Voxel))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
10-shot image generationonSemanticKITTI
mIoU (1% Labels)· 2019-06-05
37.4
best: 61.1 (PLE (Voxel))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
10-shot image generationonSemanticKITTI
mIoU (10% Labels)· 2019-06-05
54.3
best: 64 (SAPCA (Cylinder3D))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
10-shot image generationonSemanticKITTI
mIoU (20% Labels)· 2019-06-05
56.6
best: 64.2 (360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation (All))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
10-shot image generationonSemanticKITTI
mIoU (50% Labels)· 2019-06-05
57.6
best: 66.1 (360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation (All))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
10-shot image generationonnuScenes
mIoU (1% Labels)· 2019-06-05
43.8
best: 62.9 (PLE (Voxel))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
10-shot image generationonnuScenes
mIoU (10% Labels)· 2019-06-05
63.9
best: 74.3 (PLE (Voxel))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
10-shot image generationonnuScenes
mIoU (20% Labels)· 2019-06-05
64.8
best: 76 (PLE (Voxel))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916
10-shot image generationonnuScenes
mIoU (50% Labels)· 2019-06-05
69.8
best: 76.1 (PLE (Voxel))
Semi-supervised semantic segmentation needs strong, varied perturbations arXiv:1906.01916