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Models/MONET-G

MONET-G

Reported on 8 benchmarks across 3 tasks · 1 paper · 2 SOTA

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

Computer Vision6 results

  • Instance SegmentationonShelf&Tote Training Dataset
    ARI· 2021-04-20
    0.11
    best: 0.55 (GENESIS-V2)
    GENESIS-V2: Inferring Unordered Object Representations without Iterative RefinementarXiv:2104.09958
  • Instance SegmentationonShapeStacks
    ARI-FG· 2021-04-20
    0.7
    best: 0.82 (AST)
    GENESIS-V2: Inferring Unordered Object Representations without Iterative RefinementarXiv:2104.09958
  • Instance SegmentationonObjectsRoom
    ARI-FG· 2021-04-20
    0.54
    best: 0.87 (AST)
    GENESIS-V2: Inferring Unordered Object Representations without Iterative RefinementarXiv:2104.09958
  • Unsupervised Object SegmentationonShelf&Tote Training Dataset
    ARI· 2021-04-20
    0.11
    best: 0.55 (GENESIS-V2)
    GENESIS-V2: Inferring Unordered Object Representations without Iterative RefinementarXiv:2104.09958
  • Unsupervised Object SegmentationonShapeStacks
    ARI-FG· 2021-04-20
    0.7
    best: 0.82 (AST)
    GENESIS-V2: Inferring Unordered Object Representations without Iterative RefinementarXiv:2104.09958
  • Unsupervised Object SegmentationonObjectsRoom
    ARI-FG· 2021-04-20
    0.54
    best: 0.87 (AST)
    GENESIS-V2: Inferring Unordered Object Representations without Iterative RefinementarXiv:2104.09958

Medical2 results

  • Image GenerationonShapeStacks
    FID· 2021-04-20
    197.8
    best: 112.7 (GENESIS-V2)
    SOTA
    GENESIS-V2: Inferring Unordered Object Representations without Iterative RefinementarXiv:2104.09958
  • Image GenerationonObjectsRoom
    FID· 2021-04-20
    205.7
    best: 52.6 (GENESIS-V2)
    SOTA
    GENESIS-V2: Inferring Unordered Object Representations without Iterative RefinementarXiv:2104.09958