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Models/CMX (B4)

CMX (B4)

Reported on 12 benchmarks across 4 tasks · 1 paper · 10 SOTA

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

Medical4 results

Semantic SegmentationonEventScape
mIoU· 2022-03-09
64.28
SOTA
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838
Semantic SegmentationonNoisy RS RGB-T Dataset
mIoU· 2022-03-09
56.1
best: 60.3 (CMNeXt (B4))
SOTA
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838
Semantic SegmentationonMFN Dataset
mIOU· 2022-03-09
59.7
best: 62.7 (RoadFormer+ (ConvNeXt-L))
SOTA
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838
Semantic SegmentationonCityscapes val
mIoU· 2022-03-09
82.6
best: 90.3 (EfficientPS (Cityscapes-fine))
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838

Audio4 results

10-shot image generationonEventScape
mIoU· 2022-03-09
64.28
SOTA
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838
10-shot image generationonNoisy RS RGB-T Dataset
mIoU· 2022-03-09
56.1
best: 60.3 (CMNeXt (B4))
SOTA
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838
10-shot image generationonMFN Dataset
mIOU· 2022-03-09
59.7
best: 62.7 (RoadFormer+ (ConvNeXt-L))
SOTA
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838
10-shot image generationonCityscapes val
mIoU· 2022-03-09
82.6
best: 90.3 (EfficientPS (Cityscapes-fine))
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838

Computer Vision2 results

Scene SegmentationonNoisy RS RGB-T Dataset
mIoU· 2022-03-09
56.1
best: 60.3 (CMNeXt (B4))
SOTA
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838
Scene SegmentationonMFN Dataset
mIOU· 2022-03-09
59.7
best: 62.7 (RoadFormer+ (ConvNeXt-L))
SOTA
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838

Methodology2 results

2D Object DetectiononNoisy RS RGB-T Dataset
mIoU· 2022-03-09
56.1
best: 60.3 (CMNeXt (B4))
SOTA
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838
2D Object DetectiononMFN Dataset
mIOU· 2022-03-09
59.7
best: 62.7 (RoadFormer+ (ConvNeXt-L))
SOTA
CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers arXiv:2203.04838