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Models/mmmmtbvs

mmmmtbvs

Reported on 16 benchmarks across 2 tasks · 1 paper

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

Computer Vision16 results

  • Instance SegmentationonA2D Sentences
    AP· 2022-04-06
    0.419
    best: 0.585 (SgMg (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Instance SegmentationonA2D Sentences
    IoU mean· 2022-04-06
    0.558
    best: 0.725 (SOC (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Instance SegmentationonA2D Sentences
    IoU overall· 2022-04-06
    0.673
    best: 0.807 (SOC (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Instance SegmentationonA2D Sentences
    Precision@0.5· 2022-04-06
    0.645
    best: 0.851 (SOC (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Instance SegmentationonA2D Sentences
    Precision@0.6· 2022-04-06
    0.597
    best: 0.827 (SOC (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Instance SegmentationonA2D Sentences
    Precision@0.7· 2022-04-06
    0.523
    best: 0.767 (SgMg (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Instance SegmentationonA2D Sentences
    Precision@0.8· 2022-04-06
    0.375
    best: 0.617 (SgMg (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Instance SegmentationonA2D Sentences
    Precision@0.9· 2022-04-06
    0.13
    best: 0.259 (SgMg (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Referring Expression SegmentationonA2D Sentences
    AP· 2022-04-06
    0.419
    best: 0.585 (SgMg (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Referring Expression SegmentationonA2D Sentences
    IoU mean· 2022-04-06
    0.558
    best: 0.725 (SOC (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Referring Expression SegmentationonA2D Sentences
    IoU overall· 2022-04-06
    0.673
    best: 0.807 (SOC (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.5· 2022-04-06
    0.645
    best: 0.851 (SOC (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.6· 2022-04-06
    0.597
    best: 0.827 (SOC (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.7· 2022-04-06
    0.523
    best: 0.767 (SgMg (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.8· 2022-04-06
    0.375
    best: 0.617 (SgMg (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.9· 2022-04-06
    0.13
    best: 0.259 (SgMg (Video-Swin-B))
    Modeling Motion with Multi-Modal Features for Text-Based Video SegmentationarXiv:2204.02547