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Models/MATNet

MATNet

Reported on 20 benchmarks across 2 tasks · 1 paper · 8 SOTA

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

Computer Vision20 results

  • VideoonDAVIS 2016 val
    F· 2020-03-09
    80.7
    best: 90.2 (DEVA (DIS))
    SOTA
    Motion-Attentive Transition for Zero-Shot Video Object SegmentationarXiv:2003.04253
  • VideoonDAVIS 2016 val
    G· 2020-03-09
    81.6
    best: 88.9 (GSANet)
    SOTA
    Motion-Attentive Transition for Zero-Shot Video Object SegmentationarXiv:2003.04253
  • VideoonDAVIS 2016 val
    J· 2020-03-09
    82.4
    best: 88.3 (GSANet)
    SOTA
    Motion-Attentive Transition for Zero-Shot Video Object SegmentationarXiv:2003.04253
  • VideoonFBMS test
    J· 2020-03-09
    76.1
    best: 84.7 (FakeFlow)
    SOTA
    Motion-Attentive Transition for Zero-Shot Video Object SegmentationarXiv:2003.04253
  • Video Object SegmentationonDAVIS 2016 val
    F· 2020-03-09
    80.7
    best: 90.2 (DEVA (DIS))
    SOTA
    Motion-Attentive Transition for Zero-Shot Video Object SegmentationarXiv:2003.04253
  • Video Object SegmentationonDAVIS 2016 val
    G· 2020-03-09
    81.6
    best: 88.9 (GSANet)
    SOTA
    Motion-Attentive Transition for Zero-Shot Video Object SegmentationarXiv:2003.04253
  • Video Object SegmentationonDAVIS 2016 val
    J· 2020-03-09
    82.4
    best: 88.3 (GSANet)
    SOTA
    Motion-Attentive Transition for Zero-Shot Video Object SegmentationarXiv:2003.04253
  • Video Object SegmentationonFBMS test
    J· 2020-03-09
    76.1
    best: 84.7 (FakeFlow)
    SOTA
    Motion-Attentive Transition for Zero-Shot Video Object SegmentationarXiv:2003.04253
  • VideoonYouTube-Objects
    J· 2020-03-09
    69
    best: 75.1 (FakeFlow)
    Motion-Attentive Transition for Zero-Shot Video Object SegmentationarXiv:2003.04253
  • Video Object SegmentationonYouTube-Objects
    J· 2020-03-09
    69
    best: 75.1 (FakeFlow)
    Motion-Attentive Transition for Zero-Shot Video Object SegmentationarXiv:2003.04253
  • VideoonDAVIS 2017 (val)
    F-measure (Mean)
    60.4
    best: 93.4 (Cutie+ (base))
  • VideoonDAVIS 2017 (val)
    F-measure (Recall)
    68.2
    best: 94.6 (STCN)
  • VideoonDAVIS 2017 (val)
    J&F
    58.6
    best: 90.7 (SAM2)
  • VideoonDAVIS 2017 (val)
    Jaccard (Mean)
    56.7
    best: 87.5 (Cutie+ (base))
  • VideoonDAVIS 2017 (val)
    Jaccard (Recall)
    65.2
    best: 91.4 (ISVOS (MS))
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Mean)
    60.4
    best: 93.4 (Cutie+ (base))
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Recall)
    68.2
    best: 94.6 (STCN)
  • Video Object SegmentationonDAVIS 2017 (val)
    J&F
    58.6
    best: 90.7 (SAM2)
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Mean)
    56.7
    best: 87.5 (Cutie+ (base))
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Recall)
    65.2
    best: 91.4 (ISVOS (MS))