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Models/Depth-aware CNN

Depth-aware CNN

Reported on 10 benchmarks across 4 tasks · 1 paper · 6 SOTA

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

Medical4 results

Semantic SegmentationonStanford2D3D - RGBD
Pixel Accuracy· 2018-03-19
65.4
best: 82.7 (ShapeConv-101)
SOTA
Depth-aware CNN for RGB-D Segmentation arXiv:1803.06791
Semantic SegmentationonStanford2D3D - RGBD
mAcc· 2018-03-19
55.5
best: 70 (ShapeConv-101)
SOTA
Depth-aware CNN for RGB-D Segmentation arXiv:1803.06791
Semantic SegmentationonStanford2D3D - RGBD
mIoU· 2018-03-19
39.5
best: 62.1 (CMX (SegFormer-B4))
SOTA
Depth-aware CNN for RGB-D Segmentation arXiv:1803.06791
Semantic SegmentationonMFN Dataset
mIOU· 2018-03-19
46.1
best: 62.7 (RoadFormer+ (ConvNeXt-L))
Depth-aware CNN for RGB-D Segmentation arXiv:1803.06791

Audio4 results

10-shot image generationonStanford2D3D - RGBD
Pixel Accuracy· 2018-03-19
65.4
best: 82.7 (ShapeConv-101)
SOTA
Depth-aware CNN for RGB-D Segmentation arXiv:1803.06791
10-shot image generationonStanford2D3D - RGBD
mAcc· 2018-03-19
55.5
best: 70 (ShapeConv-101)
SOTA
Depth-aware CNN for RGB-D Segmentation arXiv:1803.06791
10-shot image generationonStanford2D3D - RGBD
mIoU· 2018-03-19
39.5
best: 62.1 (CMX (SegFormer-B4))
SOTA
Depth-aware CNN for RGB-D Segmentation arXiv:1803.06791
10-shot image generationonMFN Dataset
mIOU· 2018-03-19
46.1
best: 62.7 (RoadFormer+ (ConvNeXt-L))
Depth-aware CNN for RGB-D Segmentation arXiv:1803.06791

Computer Vision1 result

Scene SegmentationonMFN Dataset
mIOU· 2018-03-19
46.1
best: 62.7 (RoadFormer+ (ConvNeXt-L))
Depth-aware CNN for RGB-D Segmentation arXiv:1803.06791

Methodology1 result

2D Object DetectiononMFN Dataset
mIOU· 2018-03-19
46.1
best: 62.7 (RoadFormer+ (ConvNeXt-L))
Depth-aware CNN for RGB-D Segmentation arXiv:1803.06791