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Models/SAMFusion

SAMFusion

Reported on 14 benchmarks across 7 tasks

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

Methodology8 results

  • 3DonCAMO
    MAE· uses extra data
    0.056
    best: 0.025 (FOCUS)
  • 3DonCAMO
    Weighted F-Measure· uses extra data
    0.833
    best: 0.904 (FOCUS)
  • 2D ClassificationonCAMO
    MAE· uses extra data
    0.056
    best: 0.025 (FOCUS)
  • 2D ClassificationonCAMO
    Weighted F-Measure· uses extra data
    0.833
    best: 0.904 (FOCUS)
  • 2D Object DetectiononCAMO
    MAE· uses extra data
    0.056
    best: 0.025 (FOCUS)
  • 2D Object DetectiononCAMO
    Weighted F-Measure· uses extra data
    0.833
    best: 0.904 (FOCUS)
  • 16konCAMO
    MAE· uses extra data
    0.056
    best: 0.025 (FOCUS)
  • 16konCAMO
    Weighted F-Measure· uses extra data
    0.833
    best: 0.904 (FOCUS)

Computer Vision6 results

  • Object DetectiononCAMO
    MAE· uses extra data
    0.056
    best: 0.025 (FOCUS)
  • Object DetectiononCAMO
    Weighted F-Measure· uses extra data
    0.833
    best: 0.904 (FOCUS)
  • Camouflaged Object SegmentationonCAMO
    MAE· uses extra data
    0.056
    best: 0.025 (FOCUS)
  • Camouflaged Object SegmentationonCAMO
    Weighted F-Measure· uses extra data
    0.833
    best: 0.904 (FOCUS)
  • Object SegmentationonCAMO
    MAE· uses extra data
    0.056
    best: 0.025 (FOCUS)
  • Object SegmentationonCAMO
    Weighted F-Measure· uses extra data
    0.833
    best: 0.904 (FOCUS)