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Models/FakeFlow

FakeFlow

Reported on 10 benchmarks across 2 tasks · 1 paper · 4 SOTA

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

Computer Vision10 results

  • VideoonYouTube-Objects
    J· 2024-07-16
    75.1
    SOTA
    Improving Unsupervised Video Object Segmentation via Fake Flow GenerationarXiv:2407.11714
  • VideoonFBMS test
    J· 2024-07-16
    84.7
    SOTA
    Improving Unsupervised Video Object Segmentation via Fake Flow GenerationarXiv:2407.11714
  • Video Object SegmentationonYouTube-Objects
    J· 2024-07-16
    75.1
    SOTA
    Improving Unsupervised Video Object Segmentation via Fake Flow GenerationarXiv:2407.11714
  • Video Object SegmentationonFBMS test
    J· 2024-07-16
    84.7
    SOTA
    Improving Unsupervised Video Object Segmentation via Fake Flow GenerationarXiv:2407.11714
  • VideoonDAVIS 2016 val
    F· 2024-07-16
    89
    best: 90.2 (DEVA (DIS))
    Improving Unsupervised Video Object Segmentation via Fake Flow GenerationarXiv:2407.11714
  • VideoonDAVIS 2016 val
    G· 2024-07-16
    88.5
    best: 88.9 (GSANet)
    Improving Unsupervised Video Object Segmentation via Fake Flow GenerationarXiv:2407.11714
  • VideoonDAVIS 2016 val
    J· 2024-07-16
    88
    best: 88.3 (GSANet)
    Improving Unsupervised Video Object Segmentation via Fake Flow GenerationarXiv:2407.11714
  • Video Object SegmentationonDAVIS 2016 val
    F· 2024-07-16
    89
    best: 90.2 (DEVA (DIS))
    Improving Unsupervised Video Object Segmentation via Fake Flow GenerationarXiv:2407.11714
  • Video Object SegmentationonDAVIS 2016 val
    G· 2024-07-16
    88.5
    best: 88.9 (GSANet)
    Improving Unsupervised Video Object Segmentation via Fake Flow GenerationarXiv:2407.11714
  • Video Object SegmentationonDAVIS 2016 val
    J· 2024-07-16
    88
    best: 88.3 (GSANet)
    Improving Unsupervised Video Object Segmentation via Fake Flow GenerationarXiv:2407.11714