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Models/RTFNet

RTFNet

Reported on 26 benchmarks across 4 tasks

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

Medical8 results

  • Semantic SegmentationonSYN-UDTIRI
    IoU
    90.5
    best: 94.11 (RoadFormer+ (B))
  • Semantic Segmentationon Synthetic Bathing Perception
    mIoU
    87.49
    best: 94.2 (CMX-SRA)
  • Semantic SegmentationonGAMUS
    mIoU
    58.26
    best: 76.38 (TIMF)
  • Semantic SegmentationonNoisy RS RGB-T Dataset
    mIoU
    48.5
    best: 60.3 (CMNeXt (B4))
  • Semantic SegmentationonKP day-night
    mIoU
    28.7
    best: 57.6 (HAPNet)
  • Semantic SegmentationonPST900
    mIoU
    57.6
    best: 89.8 (SHIFNet)
  • Semantic SegmentationonRGB-T-Glass-Segmentation
    MAE
    0.058
    best: 0.024 (RGB-T-Glass-Segmentation)
  • Semantic SegmentationonMFN Dataset
    mIOU
    53.2
    best: 62.7 (RoadFormer+ (ConvNeXt-L))

Audio8 results

  • 10-shot image generationonSYN-UDTIRI
    IoU
    90.5
    best: 94.11 (RoadFormer+ (B))
  • 10-shot image generationon Synthetic Bathing Perception
    mIoU
    87.49
    best: 94.2 (CMX-SRA)
  • 10-shot image generationonGAMUS
    mIoU
    58.26
    best: 76.38 (TIMF)
  • 10-shot image generationonNoisy RS RGB-T Dataset
    mIoU
    48.5
    best: 60.3 (CMNeXt (B4))
  • 10-shot image generationonKP day-night
    mIoU
    28.7
    best: 57.6 (HAPNet)
  • 10-shot image generationonPST900
    mIoU
    57.6
    best: 89.8 (SHIFNet)
  • 10-shot image generationonRGB-T-Glass-Segmentation
    MAE
    0.058
    best: 0.024 (RGB-T-Glass-Segmentation)
  • 10-shot image generationonMFN Dataset
    mIOU
    53.2
    best: 62.7 (RoadFormer+ (ConvNeXt-L))

Computer Vision5 results

  • Scene SegmentationonNoisy RS RGB-T Dataset
    mIoU
    48.5
    best: 60.3 (CMNeXt (B4))
  • Scene SegmentationonKP day-night
    mIoU
    28.7
    best: 57.6 (HAPNet)
  • Scene SegmentationonPST900
    mIoU
    57.6
    best: 89.8 (SHIFNet)
  • Scene SegmentationonRGB-T-Glass-Segmentation
    MAE
    0.058
    best: 0.024 (RGB-T-Glass-Segmentation)
  • Scene SegmentationonMFN Dataset
    mIOU
    53.2
    best: 62.7 (RoadFormer+ (ConvNeXt-L))

Methodology5 results

  • 2D Object DetectiononNoisy RS RGB-T Dataset
    mIoU
    48.5
    best: 60.3 (CMNeXt (B4))
  • 2D Object DetectiononKP day-night
    mIoU
    28.7
    best: 57.6 (HAPNet)
  • 2D Object DetectiononPST900
    mIoU
    57.6
    best: 89.8 (SHIFNet)
  • 2D Object DetectiononRGB-T-Glass-Segmentation
    MAE
    0.058
    best: 0.024 (RGB-T-Glass-Segmentation)
  • 2D Object DetectiononMFN Dataset
    mIOU
    53.2
    best: 62.7 (RoadFormer+ (ConvNeXt-L))