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Models/3DC-Seg

3DC-Seg

Reported on 10 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 Vision10 results

  • VideoonDAVIS 2016
    F-Score· uses extra data· 2020-08-26
    84.7
    best: 94.7 (AOC-MF (val))
    SOTA
    Making a Case for 3D Convolutions for Object Segmentation in VideosarXiv:2008.11516
  • VideoonDAVIS 2016 val
    F· 2020-08-26
    84.7
    best: 90.2 (DEVA (DIS))
    SOTA
    Making a Case for 3D Convolutions for Object Segmentation in VideosarXiv:2008.11516
  • VideoonDAVIS 2016 val
    G· 2020-08-26
    84.5
    best: 88.9 (GSANet)
    SOTA
    Making a Case for 3D Convolutions for Object Segmentation in VideosarXiv:2008.11516
  • VideoonDAVIS 2016 val
    J· 2020-08-26
    84.3
    best: 88.3 (GSANet)
    SOTA
    Making a Case for 3D Convolutions for Object Segmentation in VideosarXiv:2008.11516
  • Video Object SegmentationonDAVIS 2016
    F-Score· uses extra data· 2020-08-26
    84.7
    best: 94.7 (AOC-MF (val))
    SOTA
    Making a Case for 3D Convolutions for Object Segmentation in VideosarXiv:2008.11516
  • Video Object SegmentationonDAVIS 2016 val
    F· 2020-08-26
    84.7
    best: 90.2 (DEVA (DIS))
    SOTA
    Making a Case for 3D Convolutions for Object Segmentation in VideosarXiv:2008.11516
  • Video Object SegmentationonDAVIS 2016 val
    G· 2020-08-26
    84.5
    best: 88.9 (GSANet)
    SOTA
    Making a Case for 3D Convolutions for Object Segmentation in VideosarXiv:2008.11516
  • Video Object SegmentationonDAVIS 2016 val
    J· 2020-08-26
    84.3
    best: 88.3 (GSANet)
    SOTA
    Making a Case for 3D Convolutions for Object Segmentation in VideosarXiv:2008.11516
  • VideoonDAVIS 2016
    Jaccard (Mean)· uses extra data· 2020-08-26
    84.3
    best: 92.5 (ISVOS (BL30K, MS))
    Making a Case for 3D Convolutions for Object Segmentation in VideosarXiv:2008.11516
  • Video Object SegmentationonDAVIS 2016
    Jaccard (Mean)· uses extra data· 2020-08-26
    84.3
    best: 92.5 (ISVOS (BL30K, MS))
    Making a Case for 3D Convolutions for Object Segmentation in VideosarXiv:2008.11516