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Models/SPD

SPD

Reported on 33 benchmarks across 9 tasks · 1 paper

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

Methodology17 results

  • Anomaly DetectiononVisA
    Detection AUROC· 2022-07-28
    87.8
    best: 99.8 (UniNet)
    SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and SegmentationarXiv:2207.14315
  • 3DonFBMS-59
    AVERAGE MAE· uses extra data
    0.125
    best: 0.028 (RealFlow)
  • 3DonFBMS-59
    MAX E-MEASURE· uses extra data
    0.804
    best: 0.926 (SSAV)
  • 3DonFBMS-59
    MAX F-MEASURE· uses extra data
    0.686
    best: 0.906 (RealFlow)
  • 3DonFBMS-59
    S-Measure· uses extra data
    0.691
    best: 0.926 (RealFlow)
  • 2D ClassificationonFBMS-59
    AVERAGE MAE· uses extra data
    0.125
    best: 0.028 (RealFlow)
  • 2D ClassificationonFBMS-59
    MAX E-MEASURE· uses extra data
    0.804
    best: 0.926 (SSAV)
  • 2D ClassificationonFBMS-59
    MAX F-MEASURE· uses extra data
    0.686
    best: 0.906 (RealFlow)
  • 2D ClassificationonFBMS-59
    S-Measure· uses extra data
    0.691
    best: 0.926 (RealFlow)
  • 2D Object DetectiononFBMS-59
    AVERAGE MAE· uses extra data
    0.125
    best: 0.028 (RealFlow)
  • 2D Object DetectiononFBMS-59
    MAX E-MEASURE· uses extra data
    0.804
    best: 0.926 (SSAV)
  • 2D Object DetectiononFBMS-59
    MAX F-MEASURE· uses extra data
    0.686
    best: 0.906 (RealFlow)
  • 2D Object DetectiononFBMS-59
    S-Measure· uses extra data
    0.691
    best: 0.926 (RealFlow)
  • 16konFBMS-59
    AVERAGE MAE· uses extra data
    0.125
    best: 0.028 (RealFlow)
  • 16konFBMS-59
    MAX E-MEASURE· uses extra data
    0.804
    best: 0.926 (SSAV)
  • 16konFBMS-59
    MAX F-MEASURE· uses extra data
    0.686
    best: 0.906 (RealFlow)
  • 16konFBMS-59
    S-Measure· uses extra data
    0.691
    best: 0.926 (RealFlow)

Computer Vision16 results

  • VideoonFBMS-59
    AVERAGE MAE· uses extra data
    0.125
    best: 0.028 (RealFlow)
  • VideoonFBMS-59
    MAX E-MEASURE· uses extra data
    0.804
    best: 0.926 (SSAV)
  • VideoonFBMS-59
    MAX F-MEASURE· uses extra data
    0.686
    best: 0.906 (RealFlow)
  • VideoonFBMS-59
    S-Measure· uses extra data
    0.691
    best: 0.926 (RealFlow)
  • Object DetectiononFBMS-59
    AVERAGE MAE· uses extra data
    0.125
    best: 0.028 (RealFlow)
  • Object DetectiononFBMS-59
    MAX E-MEASURE· uses extra data
    0.804
    best: 0.926 (SSAV)
  • Object DetectiononFBMS-59
    MAX F-MEASURE· uses extra data
    0.686
    best: 0.906 (RealFlow)
  • Object DetectiononFBMS-59
    S-Measure· uses extra data
    0.691
    best: 0.926 (RealFlow)
  • Video Object SegmentationonFBMS-59
    AVERAGE MAE· uses extra data
    0.125
    best: 0.028 (RealFlow)
  • Video Object SegmentationonFBMS-59
    MAX E-MEASURE· uses extra data
    0.804
    best: 0.926 (SSAV)
  • Video Object SegmentationonFBMS-59
    MAX F-MEASURE· uses extra data
    0.686
    best: 0.906 (RealFlow)
  • Video Object SegmentationonFBMS-59
    S-Measure· uses extra data
    0.691
    best: 0.926 (RealFlow)
  • RGB Salient Object DetectiononFBMS-59
    AVERAGE MAE· uses extra data
    0.125
    best: 0.028 (RealFlow)
  • RGB Salient Object DetectiononFBMS-59
    MAX E-MEASURE· uses extra data
    0.804
    best: 0.926 (SSAV)
  • RGB Salient Object DetectiononFBMS-59
    MAX F-MEASURE· uses extra data
    0.686
    best: 0.906 (RealFlow)
  • RGB Salient Object DetectiononFBMS-59
    S-Measure· uses extra data
    0.691
    best: 0.926 (RealFlow)