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Models/MTTR (w=10)

MTTR (w=10)

Reported on 32 benchmarks across 2 tasks · 1 paper · 24 SOTA

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

Computer Vision32 results

  • Instance SegmentationonA2D Sentences
    AP· 2021-11-29
    0.461
    best: 0.585 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonA2D Sentences
    IoU overall· 2021-11-29
    0.72
    best: 0.807 (SOC (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonA2D Sentences
    Precision@0.5· 2021-11-29
    0.754
    best: 0.851 (SOC (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonA2D Sentences
    Precision@0.6· 2021-11-29
    0.712
    best: 0.827 (SOC (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonA2D Sentences
    Precision@0.7· 2021-11-29
    0.638
    best: 0.767 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonJ-HMDB
    AP· 2021-11-29
    0.392
    best: 0.45 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonJ-HMDB
    IoU mean· 2021-11-29
    0.698
    best: 0.725 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonJ-HMDB
    IoU overall· 2021-11-29
    0.701
    best: 0.737 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonJ-HMDB
    Precision@0.5· 2021-11-29
    0.939
    best: 0.972 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonJ-HMDB
    Precision@0.6· 2021-11-29
    0.852
    best: 0.917 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonJ-HMDB
    Precision@0.7· 2021-11-29
    0.616
    best: 0.714 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonJ-HMDB
    Precision@0.8· 2021-11-29
    0.166
    best: 0.225 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonA2D Sentences
    AP· 2021-11-29
    0.461
    best: 0.585 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonA2D Sentences
    IoU overall· 2021-11-29
    0.72
    best: 0.807 (SOC (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.5· 2021-11-29
    0.754
    best: 0.851 (SOC (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.6· 2021-11-29
    0.712
    best: 0.827 (SOC (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.7· 2021-11-29
    0.638
    best: 0.767 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonJ-HMDB
    AP· 2021-11-29
    0.392
    best: 0.45 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonJ-HMDB
    IoU mean· 2021-11-29
    0.698
    best: 0.725 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonJ-HMDB
    IoU overall· 2021-11-29
    0.701
    best: 0.737 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonJ-HMDB
    Precision@0.5· 2021-11-29
    0.939
    best: 0.972 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonJ-HMDB
    Precision@0.6· 2021-11-29
    0.852
    best: 0.917 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonJ-HMDB
    Precision@0.7· 2021-11-29
    0.616
    best: 0.714 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonJ-HMDB
    Precision@0.8· 2021-11-29
    0.166
    best: 0.225 (SgMg (Video-Swin-B))
    SOTA
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonA2D Sentences
    IoU mean· 2021-11-29
    0.64
    best: 0.725 (SOC (Video-Swin-B))
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonA2D Sentences
    Precision@0.8· 2021-11-29
    0.485
    best: 0.617 (SgMg (Video-Swin-B))
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonA2D Sentences
    Precision@0.9· 2021-11-29
    0.169
    best: 0.259 (SgMg (Video-Swin-B))
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Instance SegmentationonJ-HMDB
    Precision@0.9· 2021-11-29
    0.001
    best: 0.4 (HINet)
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonA2D Sentences
    IoU mean· 2021-11-29
    0.64
    best: 0.725 (SOC (Video-Swin-B))
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.8· 2021-11-29
    0.485
    best: 0.617 (SgMg (Video-Swin-B))
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.9· 2021-11-29
    0.169
    best: 0.259 (SgMg (Video-Swin-B))
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821
  • Referring Expression SegmentationonJ-HMDB
    Precision@0.9· 2021-11-29
    0.001
    best: 0.4 (HINet)
    End-to-End Referring Video Object Segmentation with Multimodal TransformersarXiv:2111.14821