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Models/CMX (SegFormer-B2)

CMX (SegFormer-B2)

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

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

Medical3 results

Semantic SegmentationonLLRGBD-synthetic
mIoU· 2022-03-09
66.52
best: 68.76 (SMMCL (SegNeXt-B))
SOTA
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838
Semantic SegmentationonStanford2D3D - RGBD
Pixel Accuracy· 2022-03-09
82.3
best: 82.7 (ShapeConv-101)
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838
Semantic SegmentationonStanford2D3D - RGBD
mIoU· 2022-03-09
61.2
best: 62.1 (CMX (SegFormer-B4))
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838

Audio3 results

10-shot image generationonLLRGBD-synthetic
mIoU· 2022-03-09
66.52
best: 68.76 (SMMCL (SegNeXt-B))
SOTA
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838
10-shot image generationonStanford2D3D - RGBD
Pixel Accuracy· 2022-03-09
82.3
best: 82.7 (ShapeConv-101)
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838
10-shot image generationonStanford2D3D - RGBD
mIoU· 2022-03-09
61.2
best: 62.1 (CMX (SegFormer-B4))
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838