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Models/MMAF-Net-152

MMAF-Net-152

Reported on 6 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.

Medical3 results

Semantic SegmentationonStanford2D3D - RGBD
Pixel Accuracy· 2019-12-25
76.5
best: 82.7 (ShapeConv-101)
SOTA
Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth Images arXiv:1912.11691
Semantic SegmentationonStanford2D3D - RGBD
mAcc· 2019-12-25
62.3
best: 70 (ShapeConv-101)
SOTA
Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth Images arXiv:1912.11691
Semantic SegmentationonStanford2D3D - RGBD
mIoU· 2019-12-25
52.9
best: 62.1 (CMX (SegFormer-B4))
SOTA
Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth Images arXiv:1912.11691

Audio3 results

10-shot image generationonStanford2D3D - RGBD
Pixel Accuracy· 2019-12-25
76.5
best: 82.7 (ShapeConv-101)
SOTA
Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth Images arXiv:1912.11691
10-shot image generationonStanford2D3D - RGBD
mAcc· 2019-12-25
62.3
best: 70 (ShapeConv-101)
SOTA
Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth Images arXiv:1912.11691
10-shot image generationonStanford2D3D - RGBD
mIoU· 2019-12-25
52.9
best: 62.1 (CMX (SegFormer-B4))
SOTA
Multi-Modal Attention-based Fusion Model for Semantic Segmentation of RGB-Depth Images arXiv:1912.11691