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Models/TIMP

TIMP

Reported on 240 benchmarks across 8 tasks · 1 paper · 48 SOTA

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

Computer Vision120 results

  • VideoonSegTrack v2
    AVERAGE MAE· uses extra data· 2019-07-25
    0.116
    best: 0.022 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • VideoonSegTrack v2
    S-Measure· uses extra data· 2019-07-25
    0.644
    best: 0.892 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • VideoonSegTrack v2
    max E-measure· uses extra data· 2019-07-25
    0.768
    best: 0.935 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • VideoonMCL
    AVERAGE MAE· uses extra data· 2019-07-25
    0.113
    best: 0.021 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • VideoonMCL
    MAX E-MEASURE· uses extra data· 2019-07-25
    0.76
    best: 0.911 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • VideoonMCL
    S-Measure· uses extra data· 2019-07-25
    0.642
    best: 0.856 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • Object DetectiononSegTrack v2
    AVERAGE MAE· uses extra data· 2019-07-25
    0.116
    best: 0.022 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • Object DetectiononSegTrack v2
    S-Measure· uses extra data· 2019-07-25
    0.644
    best: 0.892 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • Object DetectiononSegTrack v2
    max E-measure· uses extra data· 2019-07-25
    0.768
    best: 0.935 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • Object DetectiononMCL
    AVERAGE MAE· uses extra data· 2019-07-25
    0.113
    best: 0.021 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • Object DetectiononMCL
    MAX E-MEASURE· uses extra data· 2019-07-25
    0.76
    best: 0.911 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • Object DetectiononMCL
    S-Measure· uses extra data· 2019-07-25
    0.642
    best: 0.856 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • Video Object SegmentationonSegTrack v2
    AVERAGE MAE· uses extra data· 2019-07-25
    0.116
    best: 0.022 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • Video Object SegmentationonSegTrack v2
    S-Measure· uses extra data· 2019-07-25
    0.644
    best: 0.892 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • Video Object SegmentationonSegTrack v2
    max E-measure· uses extra data· 2019-07-25
    0.768
    best: 0.935 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • Video Object SegmentationonMCL
    AVERAGE MAE· uses extra data· 2019-07-25
    0.113
    best: 0.021 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • Video Object SegmentationonMCL
    MAX E-MEASURE· uses extra data· 2019-07-25
    0.76
    best: 0.911 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • Video Object SegmentationonMCL
    S-Measure· uses extra data· 2019-07-25
    0.642
    best: 0.856 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • RGB Salient Object DetectiononSegTrack v2
    AVERAGE MAE· uses extra data· 2019-07-25
    0.116
    best: 0.022 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • RGB Salient Object DetectiononSegTrack v2
    S-Measure· uses extra data· 2019-07-25
    0.644
    best: 0.892 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • RGB Salient Object DetectiononSegTrack v2
    max E-measure· uses extra data· 2019-07-25
    0.768
    best: 0.935 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • RGB Salient Object DetectiononMCL
    AVERAGE MAE· uses extra data· 2019-07-25
    0.113
    best: 0.021 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • RGB Salient Object DetectiononMCL
    MAX E-MEASURE· uses extra data· 2019-07-25
    0.76
    best: 0.911 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • RGB Salient Object DetectiononMCL
    S-Measure· uses extra data· 2019-07-25
    0.642
    best: 0.856 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • VideoonDAVSOD-easy35
    Average MAE· uses extra data
    0.206
    best: 0.066 (RealFlow)
  • VideoonDAVSOD-easy35
    S-Measure· uses extra data
    0.534
    best: 0.803 (RealFlow)
  • VideoonDAVSOD-easy35
    max E-Measure· uses extra data
    0.582
    best: 0.806 (SSAV)
  • VideoonFBMS-59
    AVERAGE MAE· uses extra data
    0.192
    best: 0.028 (RealFlow)
  • VideoonFBMS-59
    MAX F-MEASURE· uses extra data
    0.465
    best: 0.906 (RealFlow)
  • VideoonFBMS-59
    S-Measure· uses extra data
    0.576
    best: 0.926 (RealFlow)
  • VideoonUVSD
    Average MAE· uses extra data
    0.171
    best: 0.018 (PDB)
  • VideoonUVSD
    S-Measure· uses extra data
    0.541
    best: 0.901 (PDB)
  • VideoonUVSD
    max E-measure· uses extra data
    0.662
    best: 0.975 (PDB)
  • VideoonVOS-T
    Average MAE· uses extra data
    0.192
    best: 0.049 (RCRNet+NER)
  • VideoonVOS-T
    S-Measure· uses extra data
    0.546
    best: 0.872 (RCRNet+NER)
  • VideoonVOS-T
    max E-measure· uses extra data
    0.64
    best: 0.856 (RCRNet+NER)
  • VideoonDAVSOD-Difficult20
    Average MAE· uses extra data
    0.19
    best: 0.107 (PDB)
  • VideoonDAVSOD-Difficult20
    S-Measure· uses extra data
    0.53
    best: 0.619 (SSAV)
  • VideoonDAVSOD-Difficult20
    max E-measure· uses extra data
    0.665
    best: 0.698 (FGRN)
  • VideoonDAVIS-2016
    AVERAGE MAE· uses extra data
    0.185
    best: 0.01 (RealFlow)
  • VideoonDAVIS-2016
    MAX E-MEASURE· uses extra data
    0.68
    best: 0.966 (MBNM)
  • VideoonDAVIS-2016
    S-Measure· uses extra data
    0.574
    best: 0.945 (RealFlow)
  • VideoonViSal
    Average MAE· uses extra data
    0.17
    best: 0.01 (RealFlow)
  • VideoonViSal
    S-Measure· uses extra data
    0.612
    best: 0.962 (RealFlow)
  • VideoonViSal
    max E-measure· uses extra data
    0.743
    best: 0.987 (UFO)
  • VideoonDAVSOD-Normal25
    Average MAE· uses extra data
    0.245
    best: 0.117 (SSAV)
  • VideoonDAVSOD-Normal25
    S-Measure· uses extra data
    0.503
    best: 0.661 (SSAV)
  • VideoonDAVSOD-Normal25
    max E-measure· uses extra data
    0.616
    best: 0.723 (SSAV)
  • Object DetectiononDAVSOD-easy35
    Average MAE· uses extra data
    0.206
    best: 0.066 (RealFlow)
  • Object DetectiononDAVSOD-easy35
    S-Measure· uses extra data
    0.534
    best: 0.803 (RealFlow)
  • Object DetectiononDAVSOD-easy35
    max E-Measure· uses extra data
    0.582
    best: 0.806 (SSAV)
  • Object DetectiononFBMS-59
    AVERAGE MAE· uses extra data
    0.192
    best: 0.028 (RealFlow)
  • Object DetectiononFBMS-59
    MAX F-MEASURE· uses extra data
    0.465
    best: 0.906 (RealFlow)
  • Object DetectiononFBMS-59
    S-Measure· uses extra data
    0.576
    best: 0.926 (RealFlow)
  • Object DetectiononUVSD
    Average MAE· uses extra data
    0.171
    best: 0.018 (PDB)
  • Object DetectiononUVSD
    S-Measure· uses extra data
    0.541
    best: 0.901 (PDB)
  • Object DetectiononUVSD
    max E-measure· uses extra data
    0.662
    best: 0.975 (PDB)
  • Object DetectiononVOS-T
    Average MAE· uses extra data
    0.192
    best: 0.049 (RCRNet+NER)
  • Object DetectiononVOS-T
    S-Measure· uses extra data
    0.546
    best: 0.872 (RCRNet+NER)
  • Object DetectiononVOS-T
    max E-measure· uses extra data
    0.64
    best: 0.856 (RCRNet+NER)
  • Object DetectiononDAVSOD-Difficult20
    Average MAE· uses extra data
    0.19
    best: 0.107 (PDB)
  • Object DetectiononDAVSOD-Difficult20
    S-Measure· uses extra data
    0.53
    best: 0.619 (SSAV)
  • Object DetectiononDAVSOD-Difficult20
    max E-measure· uses extra data
    0.665
    best: 0.698 (FGRN)
  • Object DetectiononDAVIS-2016
    AVERAGE MAE· uses extra data
    0.185
    best: 0.01 (RealFlow)
  • Object DetectiononDAVIS-2016
    MAX E-MEASURE· uses extra data
    0.68
    best: 0.966 (MBNM)
  • Object DetectiononDAVIS-2016
    S-Measure· uses extra data
    0.574
    best: 0.945 (RealFlow)
  • Object DetectiononViSal
    Average MAE· uses extra data
    0.17
    best: 0.01 (RealFlow)
  • Object DetectiononViSal
    S-Measure· uses extra data
    0.612
    best: 0.962 (RealFlow)
  • Object DetectiononViSal
    max E-measure· uses extra data
    0.743
    best: 0.987 (UFO)
  • Object DetectiononDAVSOD-Normal25
    Average MAE· uses extra data
    0.245
    best: 0.117 (SSAV)
  • Object DetectiononDAVSOD-Normal25
    S-Measure· uses extra data
    0.503
    best: 0.661 (SSAV)
  • Object DetectiononDAVSOD-Normal25
    max E-measure· uses extra data
    0.616
    best: 0.723 (SSAV)
  • Video Object SegmentationonDAVSOD-easy35
    Average MAE· uses extra data
    0.206
    best: 0.066 (RealFlow)
  • Video Object SegmentationonDAVSOD-easy35
    S-Measure· uses extra data
    0.534
    best: 0.803 (RealFlow)
  • Video Object SegmentationonDAVSOD-easy35
    max E-Measure· uses extra data
    0.582
    best: 0.806 (SSAV)
  • Video Object SegmentationonFBMS-59
    AVERAGE MAE· uses extra data
    0.192
    best: 0.028 (RealFlow)
  • Video Object SegmentationonFBMS-59
    MAX F-MEASURE· uses extra data
    0.465
    best: 0.906 (RealFlow)
  • Video Object SegmentationonFBMS-59
    S-Measure· uses extra data
    0.576
    best: 0.926 (RealFlow)
  • Video Object SegmentationonUVSD
    Average MAE· uses extra data
    0.171
    best: 0.018 (PDB)
  • Video Object SegmentationonUVSD
    S-Measure· uses extra data
    0.541
    best: 0.901 (PDB)
  • Video Object SegmentationonUVSD
    max E-measure· uses extra data
    0.662
    best: 0.975 (PDB)
  • Video Object SegmentationonVOS-T
    Average MAE· uses extra data
    0.192
    best: 0.049 (RCRNet+NER)
  • Video Object SegmentationonVOS-T
    S-Measure· uses extra data
    0.546
    best: 0.872 (RCRNet+NER)
  • Video Object SegmentationonVOS-T
    max E-measure· uses extra data
    0.64
    best: 0.856 (RCRNet+NER)
  • Video Object SegmentationonDAVSOD-Difficult20
    Average MAE· uses extra data
    0.19
    best: 0.107 (PDB)
  • Video Object SegmentationonDAVSOD-Difficult20
    S-Measure· uses extra data
    0.53
    best: 0.619 (SSAV)
  • Video Object SegmentationonDAVSOD-Difficult20
    max E-measure· uses extra data
    0.665
    best: 0.698 (FGRN)
  • Video Object SegmentationonDAVIS-2016
    AVERAGE MAE· uses extra data
    0.185
    best: 0.01 (RealFlow)
  • Video Object SegmentationonDAVIS-2016
    MAX E-MEASURE· uses extra data
    0.68
    best: 0.966 (MBNM)
  • Video Object SegmentationonDAVIS-2016
    S-Measure· uses extra data
    0.574
    best: 0.945 (RealFlow)
  • Video Object SegmentationonViSal
    Average MAE· uses extra data
    0.17
    best: 0.01 (RealFlow)
  • Video Object SegmentationonViSal
    S-Measure· uses extra data
    0.612
    best: 0.962 (RealFlow)
  • Video Object SegmentationonViSal
    max E-measure· uses extra data
    0.743
    best: 0.987 (UFO)
  • Video Object SegmentationonDAVSOD-Normal25
    Average MAE· uses extra data
    0.245
    best: 0.117 (SSAV)
  • Video Object SegmentationonDAVSOD-Normal25
    S-Measure· uses extra data
    0.503
    best: 0.661 (SSAV)
  • Video Object SegmentationonDAVSOD-Normal25
    max E-measure· uses extra data
    0.616
    best: 0.723 (SSAV)
  • RGB Salient Object DetectiononDAVSOD-easy35
    Average MAE· uses extra data
    0.206
    best: 0.066 (RealFlow)
  • RGB Salient Object DetectiononDAVSOD-easy35
    S-Measure· uses extra data
    0.534
    best: 0.803 (RealFlow)
  • RGB Salient Object DetectiononDAVSOD-easy35
    max E-Measure· uses extra data
    0.582
    best: 0.806 (SSAV)
  • RGB Salient Object DetectiononFBMS-59
    AVERAGE MAE· uses extra data
    0.192
    best: 0.028 (RealFlow)
  • RGB Salient Object DetectiononFBMS-59
    MAX F-MEASURE· uses extra data
    0.465
    best: 0.906 (RealFlow)
  • RGB Salient Object DetectiononFBMS-59
    S-Measure· uses extra data
    0.576
    best: 0.926 (RealFlow)
  • RGB Salient Object DetectiononUVSD
    Average MAE· uses extra data
    0.171
    best: 0.018 (PDB)
  • RGB Salient Object DetectiononUVSD
    S-Measure· uses extra data
    0.541
    best: 0.901 (PDB)
  • RGB Salient Object DetectiononUVSD
    max E-measure· uses extra data
    0.662
    best: 0.975 (PDB)
  • RGB Salient Object DetectiononVOS-T
    Average MAE· uses extra data
    0.192
    best: 0.049 (RCRNet+NER)
  • RGB Salient Object DetectiononVOS-T
    S-Measure· uses extra data
    0.546
    best: 0.872 (RCRNet+NER)
  • RGB Salient Object DetectiononVOS-T
    max E-measure· uses extra data
    0.64
    best: 0.856 (RCRNet+NER)
  • RGB Salient Object DetectiononDAVSOD-Difficult20
    Average MAE· uses extra data
    0.19
    best: 0.107 (PDB)
  • RGB Salient Object DetectiononDAVSOD-Difficult20
    S-Measure· uses extra data
    0.53
    best: 0.619 (SSAV)
  • RGB Salient Object DetectiononDAVSOD-Difficult20
    max E-measure· uses extra data
    0.665
    best: 0.698 (FGRN)
  • RGB Salient Object DetectiononDAVIS-2016
    AVERAGE MAE· uses extra data
    0.185
    best: 0.01 (RealFlow)
  • RGB Salient Object DetectiononDAVIS-2016
    MAX E-MEASURE· uses extra data
    0.68
    best: 0.966 (MBNM)
  • RGB Salient Object DetectiononDAVIS-2016
    S-Measure· uses extra data
    0.574
    best: 0.945 (RealFlow)
  • RGB Salient Object DetectiononViSal
    Average MAE· uses extra data
    0.17
    best: 0.01 (RealFlow)
  • RGB Salient Object DetectiononViSal
    S-Measure· uses extra data
    0.612
    best: 0.962 (RealFlow)
  • RGB Salient Object DetectiononViSal
    max E-measure· uses extra data
    0.743
    best: 0.987 (UFO)
  • RGB Salient Object DetectiononDAVSOD-Normal25
    Average MAE· uses extra data
    0.245
    best: 0.117 (SSAV)
  • RGB Salient Object DetectiononDAVSOD-Normal25
    S-Measure· uses extra data
    0.503
    best: 0.661 (SSAV)
  • RGB Salient Object DetectiononDAVSOD-Normal25
    max E-measure· uses extra data
    0.616
    best: 0.723 (SSAV)

Methodology120 results

  • 3DonSegTrack v2
    AVERAGE MAE· uses extra data· 2019-07-25
    0.116
    best: 0.022 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 3DonSegTrack v2
    S-Measure· uses extra data· 2019-07-25
    0.644
    best: 0.892 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 3DonSegTrack v2
    max E-measure· uses extra data· 2019-07-25
    0.768
    best: 0.935 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 3DonMCL
    AVERAGE MAE· uses extra data· 2019-07-25
    0.113
    best: 0.021 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 3DonMCL
    MAX E-MEASURE· uses extra data· 2019-07-25
    0.76
    best: 0.911 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 3DonMCL
    S-Measure· uses extra data· 2019-07-25
    0.642
    best: 0.856 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 2D ClassificationonSegTrack v2
    AVERAGE MAE· uses extra data· 2019-07-25
    0.116
    best: 0.022 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 2D ClassificationonSegTrack v2
    S-Measure· uses extra data· 2019-07-25
    0.644
    best: 0.892 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 2D ClassificationonSegTrack v2
    max E-measure· uses extra data· 2019-07-25
    0.768
    best: 0.935 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 2D ClassificationonMCL
    AVERAGE MAE· uses extra data· 2019-07-25
    0.113
    best: 0.021 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 2D ClassificationonMCL
    MAX E-MEASURE· uses extra data· 2019-07-25
    0.76
    best: 0.911 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 2D ClassificationonMCL
    S-Measure· uses extra data· 2019-07-25
    0.642
    best: 0.856 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 2D Object DetectiononSegTrack v2
    AVERAGE MAE· uses extra data· 2019-07-25
    0.116
    best: 0.022 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 2D Object DetectiononSegTrack v2
    S-Measure· uses extra data· 2019-07-25
    0.644
    best: 0.892 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 2D Object DetectiononSegTrack v2
    max E-measure· uses extra data· 2019-07-25
    0.768
    best: 0.935 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 2D Object DetectiononMCL
    AVERAGE MAE· uses extra data· 2019-07-25
    0.113
    best: 0.021 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 2D Object DetectiononMCL
    MAX E-MEASURE· uses extra data· 2019-07-25
    0.76
    best: 0.911 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 2D Object DetectiononMCL
    S-Measure· uses extra data· 2019-07-25
    0.642
    best: 0.856 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 16konSegTrack v2
    AVERAGE MAE· uses extra data· 2019-07-25
    0.116
    best: 0.022 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 16konSegTrack v2
    S-Measure· uses extra data· 2019-07-25
    0.644
    best: 0.892 (UFO)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 16konSegTrack v2
    max E-measure· uses extra data· 2019-07-25
    0.768
    best: 0.935 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 16konMCL
    AVERAGE MAE· uses extra data· 2019-07-25
    0.113
    best: 0.021 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 16konMCL
    MAX E-MEASURE· uses extra data· 2019-07-25
    0.76
    best: 0.911 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 16konMCL
    S-Measure· uses extra data· 2019-07-25
    0.642
    best: 0.856 (PDB)
    SOTA
    Time Masking: Leveraging Temporal Information in Spoken Dialogue SystemsarXiv:1907.11315
  • 3DonDAVSOD-easy35
    Average MAE· uses extra data
    0.206
    best: 0.066 (RealFlow)
  • 3DonDAVSOD-easy35
    S-Measure· uses extra data
    0.534
    best: 0.803 (RealFlow)
  • 3DonDAVSOD-easy35
    max E-Measure· uses extra data
    0.582
    best: 0.806 (SSAV)
  • 3DonFBMS-59
    AVERAGE MAE· uses extra data
    0.192
    best: 0.028 (RealFlow)
  • 3DonFBMS-59
    MAX F-MEASURE· uses extra data
    0.465
    best: 0.906 (RealFlow)
  • 3DonFBMS-59
    S-Measure· uses extra data
    0.576
    best: 0.926 (RealFlow)
  • 3DonUVSD
    Average MAE· uses extra data
    0.171
    best: 0.018 (PDB)
  • 3DonUVSD
    S-Measure· uses extra data
    0.541
    best: 0.901 (PDB)
  • 3DonUVSD
    max E-measure· uses extra data
    0.662
    best: 0.975 (PDB)
  • 3DonVOS-T
    Average MAE· uses extra data
    0.192
    best: 0.049 (RCRNet+NER)
  • 3DonVOS-T
    S-Measure· uses extra data
    0.546
    best: 0.872 (RCRNet+NER)
  • 3DonVOS-T
    max E-measure· uses extra data
    0.64
    best: 0.856 (RCRNet+NER)
  • 3DonDAVSOD-Difficult20
    Average MAE· uses extra data
    0.19
    best: 0.107 (PDB)
  • 3DonDAVSOD-Difficult20
    S-Measure· uses extra data
    0.53
    best: 0.619 (SSAV)
  • 3DonDAVSOD-Difficult20
    max E-measure· uses extra data
    0.665
    best: 0.698 (FGRN)
  • 3DonDAVIS-2016
    AVERAGE MAE· uses extra data
    0.185
    best: 0.01 (RealFlow)
  • 3DonDAVIS-2016
    MAX E-MEASURE· uses extra data
    0.68
    best: 0.966 (MBNM)
  • 3DonDAVIS-2016
    S-Measure· uses extra data
    0.574
    best: 0.945 (RealFlow)
  • 3DonViSal
    Average MAE· uses extra data
    0.17
    best: 0.01 (RealFlow)
  • 3DonViSal
    S-Measure· uses extra data
    0.612
    best: 0.962 (RealFlow)
  • 3DonViSal
    max E-measure· uses extra data
    0.743
    best: 0.987 (UFO)
  • 3DonDAVSOD-Normal25
    Average MAE· uses extra data
    0.245
    best: 0.117 (SSAV)
  • 3DonDAVSOD-Normal25
    S-Measure· uses extra data
    0.503
    best: 0.661 (SSAV)
  • 3DonDAVSOD-Normal25
    max E-measure· uses extra data
    0.616
    best: 0.723 (SSAV)
  • 2D ClassificationonDAVSOD-easy35
    Average MAE· uses extra data
    0.206
    best: 0.066 (RealFlow)
  • 2D ClassificationonDAVSOD-easy35
    S-Measure· uses extra data
    0.534
    best: 0.803 (RealFlow)
  • 2D ClassificationonDAVSOD-easy35
    max E-Measure· uses extra data
    0.582
    best: 0.806 (SSAV)
  • 2D ClassificationonFBMS-59
    AVERAGE MAE· uses extra data
    0.192
    best: 0.028 (RealFlow)
  • 2D ClassificationonFBMS-59
    MAX F-MEASURE· uses extra data
    0.465
    best: 0.906 (RealFlow)
  • 2D ClassificationonFBMS-59
    S-Measure· uses extra data
    0.576
    best: 0.926 (RealFlow)
  • 2D ClassificationonUVSD
    Average MAE· uses extra data
    0.171
    best: 0.018 (PDB)
  • 2D ClassificationonUVSD
    S-Measure· uses extra data
    0.541
    best: 0.901 (PDB)
  • 2D ClassificationonUVSD
    max E-measure· uses extra data
    0.662
    best: 0.975 (PDB)
  • 2D ClassificationonVOS-T
    Average MAE· uses extra data
    0.192
    best: 0.049 (RCRNet+NER)
  • 2D ClassificationonVOS-T
    S-Measure· uses extra data
    0.546
    best: 0.872 (RCRNet+NER)
  • 2D ClassificationonVOS-T
    max E-measure· uses extra data
    0.64
    best: 0.856 (RCRNet+NER)
  • 2D ClassificationonDAVSOD-Difficult20
    Average MAE· uses extra data
    0.19
    best: 0.107 (PDB)
  • 2D ClassificationonDAVSOD-Difficult20
    S-Measure· uses extra data
    0.53
    best: 0.619 (SSAV)
  • 2D ClassificationonDAVSOD-Difficult20
    max E-measure· uses extra data
    0.665
    best: 0.698 (FGRN)
  • 2D ClassificationonDAVIS-2016
    AVERAGE MAE· uses extra data
    0.185
    best: 0.01 (RealFlow)
  • 2D ClassificationonDAVIS-2016
    MAX E-MEASURE· uses extra data
    0.68
    best: 0.966 (MBNM)
  • 2D ClassificationonDAVIS-2016
    S-Measure· uses extra data
    0.574
    best: 0.945 (RealFlow)
  • 2D ClassificationonViSal
    Average MAE· uses extra data
    0.17
    best: 0.01 (RealFlow)
  • 2D ClassificationonViSal
    S-Measure· uses extra data
    0.612
    best: 0.962 (RealFlow)
  • 2D ClassificationonViSal
    max E-measure· uses extra data
    0.743
    best: 0.987 (UFO)
  • 2D ClassificationonDAVSOD-Normal25
    Average MAE· uses extra data
    0.245
    best: 0.117 (SSAV)
  • 2D ClassificationonDAVSOD-Normal25
    S-Measure· uses extra data
    0.503
    best: 0.661 (SSAV)
  • 2D ClassificationonDAVSOD-Normal25
    max E-measure· uses extra data
    0.616
    best: 0.723 (SSAV)
  • 2D Object DetectiononDAVSOD-easy35
    Average MAE· uses extra data
    0.206
    best: 0.066 (RealFlow)
  • 2D Object DetectiononDAVSOD-easy35
    S-Measure· uses extra data
    0.534
    best: 0.803 (RealFlow)
  • 2D Object DetectiononDAVSOD-easy35
    max E-Measure· uses extra data
    0.582
    best: 0.806 (SSAV)
  • 2D Object DetectiononFBMS-59
    AVERAGE MAE· uses extra data
    0.192
    best: 0.028 (RealFlow)
  • 2D Object DetectiononFBMS-59
    MAX F-MEASURE· uses extra data
    0.465
    best: 0.906 (RealFlow)
  • 2D Object DetectiononFBMS-59
    S-Measure· uses extra data
    0.576
    best: 0.926 (RealFlow)
  • 2D Object DetectiononUVSD
    Average MAE· uses extra data
    0.171
    best: 0.018 (PDB)
  • 2D Object DetectiononUVSD
    S-Measure· uses extra data
    0.541
    best: 0.901 (PDB)
  • 2D Object DetectiononUVSD
    max E-measure· uses extra data
    0.662
    best: 0.975 (PDB)
  • 2D Object DetectiononVOS-T
    Average MAE· uses extra data
    0.192
    best: 0.049 (RCRNet+NER)
  • 2D Object DetectiononVOS-T
    S-Measure· uses extra data
    0.546
    best: 0.872 (RCRNet+NER)
  • 2D Object DetectiononVOS-T
    max E-measure· uses extra data
    0.64
    best: 0.856 (RCRNet+NER)
  • 2D Object DetectiononDAVSOD-Difficult20
    Average MAE· uses extra data
    0.19
    best: 0.107 (PDB)
  • 2D Object DetectiononDAVSOD-Difficult20
    S-Measure· uses extra data
    0.53
    best: 0.619 (SSAV)
  • 2D Object DetectiononDAVSOD-Difficult20
    max E-measure· uses extra data
    0.665
    best: 0.698 (FGRN)
  • 2D Object DetectiononDAVIS-2016
    AVERAGE MAE· uses extra data
    0.185
    best: 0.01 (RealFlow)
  • 2D Object DetectiononDAVIS-2016
    MAX E-MEASURE· uses extra data
    0.68
    best: 0.966 (MBNM)
  • 2D Object DetectiononDAVIS-2016
    S-Measure· uses extra data
    0.574
    best: 0.945 (RealFlow)
  • 2D Object DetectiononViSal
    Average MAE· uses extra data
    0.17
    best: 0.01 (RealFlow)
  • 2D Object DetectiononViSal
    S-Measure· uses extra data
    0.612
    best: 0.962 (RealFlow)
  • 2D Object DetectiononViSal
    max E-measure· uses extra data
    0.743
    best: 0.987 (UFO)
  • 2D Object DetectiononDAVSOD-Normal25
    Average MAE· uses extra data
    0.245
    best: 0.117 (SSAV)
  • 2D Object DetectiononDAVSOD-Normal25
    S-Measure· uses extra data
    0.503
    best: 0.661 (SSAV)
  • 2D Object DetectiononDAVSOD-Normal25
    max E-measure· uses extra data
    0.616
    best: 0.723 (SSAV)
  • 16konDAVSOD-easy35
    Average MAE· uses extra data
    0.206
    best: 0.066 (RealFlow)
  • 16konDAVSOD-easy35
    S-Measure· uses extra data
    0.534
    best: 0.803 (RealFlow)
  • 16konDAVSOD-easy35
    max E-Measure· uses extra data
    0.582
    best: 0.806 (SSAV)
  • 16konFBMS-59
    AVERAGE MAE· uses extra data
    0.192
    best: 0.028 (RealFlow)
  • 16konFBMS-59
    MAX F-MEASURE· uses extra data
    0.465
    best: 0.906 (RealFlow)
  • 16konFBMS-59
    S-Measure· uses extra data
    0.576
    best: 0.926 (RealFlow)
  • 16konUVSD
    Average MAE· uses extra data
    0.171
    best: 0.018 (PDB)
  • 16konUVSD
    S-Measure· uses extra data
    0.541
    best: 0.901 (PDB)
  • 16konUVSD
    max E-measure· uses extra data
    0.662
    best: 0.975 (PDB)
  • 16konVOS-T
    Average MAE· uses extra data
    0.192
    best: 0.049 (RCRNet+NER)
  • 16konVOS-T
    S-Measure· uses extra data
    0.546
    best: 0.872 (RCRNet+NER)
  • 16konVOS-T
    max E-measure· uses extra data
    0.64
    best: 0.856 (RCRNet+NER)
  • 16konDAVSOD-Difficult20
    Average MAE· uses extra data
    0.19
    best: 0.107 (PDB)
  • 16konDAVSOD-Difficult20
    S-Measure· uses extra data
    0.53
    best: 0.619 (SSAV)
  • 16konDAVSOD-Difficult20
    max E-measure· uses extra data
    0.665
    best: 0.698 (FGRN)
  • 16konDAVIS-2016
    AVERAGE MAE· uses extra data
    0.185
    best: 0.01 (RealFlow)
  • 16konDAVIS-2016
    MAX E-MEASURE· uses extra data
    0.68
    best: 0.966 (MBNM)
  • 16konDAVIS-2016
    S-Measure· uses extra data
    0.574
    best: 0.945 (RealFlow)
  • 16konViSal
    Average MAE· uses extra data
    0.17
    best: 0.01 (RealFlow)
  • 16konViSal
    S-Measure· uses extra data
    0.612
    best: 0.962 (RealFlow)
  • 16konViSal
    max E-measure· uses extra data
    0.743
    best: 0.987 (UFO)
  • 16konDAVSOD-Normal25
    Average MAE· uses extra data
    0.245
    best: 0.117 (SSAV)
  • 16konDAVSOD-Normal25
    S-Measure· uses extra data
    0.503
    best: 0.661 (SSAV)
  • 16konDAVSOD-Normal25
    max E-measure· uses extra data
    0.616
    best: 0.723 (SSAV)