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Models/LTSI

LTSI

Reported on 32 benchmarks across 8 tasks

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

Computer Vision16 results

  • VideoonFBMS-59
    AVERAGE MAE· uses extra data
    0.087
    best: 0.028 (RealFlow)
  • VideoonFBMS-59
    MAX E-MEASURE· uses extra data
    0.871
    best: 0.926 (SSAV)
  • VideoonFBMS-59
    MAX F-MEASURE· uses extra data
    0.799
    best: 0.906 (RealFlow)
  • VideoonFBMS-59
    S-Measure· uses extra data
    0.805
    best: 0.926 (RealFlow)
  • Object DetectiononFBMS-59
    AVERAGE MAE· uses extra data
    0.087
    best: 0.028 (RealFlow)
  • Object DetectiononFBMS-59
    MAX E-MEASURE· uses extra data
    0.871
    best: 0.926 (SSAV)
  • Object DetectiononFBMS-59
    MAX F-MEASURE· uses extra data
    0.799
    best: 0.906 (RealFlow)
  • Object DetectiononFBMS-59
    S-Measure· uses extra data
    0.805
    best: 0.926 (RealFlow)
  • Video Object SegmentationonFBMS-59
    AVERAGE MAE· uses extra data
    0.087
    best: 0.028 (RealFlow)
  • Video Object SegmentationonFBMS-59
    MAX E-MEASURE· uses extra data
    0.871
    best: 0.926 (SSAV)
  • Video Object SegmentationonFBMS-59
    MAX F-MEASURE· uses extra data
    0.799
    best: 0.906 (RealFlow)
  • Video Object SegmentationonFBMS-59
    S-Measure· uses extra data
    0.805
    best: 0.926 (RealFlow)
  • RGB Salient Object DetectiononFBMS-59
    AVERAGE MAE· uses extra data
    0.087
    best: 0.028 (RealFlow)
  • RGB Salient Object DetectiononFBMS-59
    MAX E-MEASURE· uses extra data
    0.871
    best: 0.926 (SSAV)
  • RGB Salient Object DetectiononFBMS-59
    MAX F-MEASURE· uses extra data
    0.799
    best: 0.906 (RealFlow)
  • RGB Salient Object DetectiononFBMS-59
    S-Measure· uses extra data
    0.805
    best: 0.926 (RealFlow)

Methodology16 results

  • 3DonFBMS-59
    AVERAGE MAE· uses extra data
    0.087
    best: 0.028 (RealFlow)
  • 3DonFBMS-59
    MAX E-MEASURE· uses extra data
    0.871
    best: 0.926 (SSAV)
  • 3DonFBMS-59
    MAX F-MEASURE· uses extra data
    0.799
    best: 0.906 (RealFlow)
  • 3DonFBMS-59
    S-Measure· uses extra data
    0.805
    best: 0.926 (RealFlow)
  • 2D ClassificationonFBMS-59
    AVERAGE MAE· uses extra data
    0.087
    best: 0.028 (RealFlow)
  • 2D ClassificationonFBMS-59
    MAX E-MEASURE· uses extra data
    0.871
    best: 0.926 (SSAV)
  • 2D ClassificationonFBMS-59
    MAX F-MEASURE· uses extra data
    0.799
    best: 0.906 (RealFlow)
  • 2D ClassificationonFBMS-59
    S-Measure· uses extra data
    0.805
    best: 0.926 (RealFlow)
  • 2D Object DetectiononFBMS-59
    AVERAGE MAE· uses extra data
    0.087
    best: 0.028 (RealFlow)
  • 2D Object DetectiononFBMS-59
    MAX E-MEASURE· uses extra data
    0.871
    best: 0.926 (SSAV)
  • 2D Object DetectiononFBMS-59
    MAX F-MEASURE· uses extra data
    0.799
    best: 0.906 (RealFlow)
  • 2D Object DetectiononFBMS-59
    S-Measure· uses extra data
    0.805
    best: 0.926 (RealFlow)
  • 16konFBMS-59
    AVERAGE MAE· uses extra data
    0.087
    best: 0.028 (RealFlow)
  • 16konFBMS-59
    MAX E-MEASURE· uses extra data
    0.871
    best: 0.926 (SSAV)
  • 16konFBMS-59
    MAX F-MEASURE· uses extra data
    0.799
    best: 0.906 (RealFlow)
  • 16konFBMS-59
    S-Measure· uses extra data
    0.805
    best: 0.926 (RealFlow)