TasksSotADatasetsPapersMethodsSubmitAbout
Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable BenchmarksAll SotADatasetsPapersMethods

Community

Submit ResultsAbout

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/Hu et al.

Hu et al.

Reported on 33 benchmarks across 3 tasks · 2 papers · 29 SOTA

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

Computer Vision33 results

  • Eyeblink detectiononHUST-LEBW
    Avg. F1 · 2019-02-21
    61.815
    best: 91.345 (Zeng et al.)
    SOTA
    Towards Real-time Eyeblink Detection in The Wild:Dataset,Theory and PracticesarXiv:1902.07891
  • Instance SegmentationonA2D Sentences
    AP· 2016-03-20
    0.132
    best: 0.585 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonA2D Sentences
    IoU mean· 2016-03-20
    0.35
    best: 0.725 (SOC (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonA2D Sentences
    IoU overall· 2016-03-20
    0.474
    best: 0.807 (SOC (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonA2D Sentences
    Precision@0.5· 2016-03-20
    0.348
    best: 0.851 (SOC (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonA2D Sentences
    Precision@0.6· 2016-03-20
    0.236
    best: 0.827 (SOC (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonA2D Sentences
    Precision@0.7· 2016-03-20
    0.133
    best: 0.767 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonA2D Sentences
    Precision@0.8· 2016-03-20
    0.033
    best: 0.617 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonJ-HMDB
    AP· 2016-03-20
    0.178
    best: 0.45 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonJ-HMDB
    IoU mean· 2016-03-20
    0.528
    best: 0.725 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonJ-HMDB
    IoU overall· 2016-03-20
    0.546
    best: 0.737 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonJ-HMDB
    Precision@0.5· 2016-03-20
    0.633
    best: 0.972 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonJ-HMDB
    Precision@0.6· 2016-03-20
    0.35
    best: 0.917 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonJ-HMDB
    Precision@0.7· 2016-03-20
    0.085
    best: 0.714 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonJ-HMDB
    Precision@0.8· 2016-03-20
    0.002
    best: 0.225 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonA2D Sentences
    AP· 2016-03-20
    0.132
    best: 0.585 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonA2D Sentences
    IoU mean· 2016-03-20
    0.35
    best: 0.725 (SOC (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonA2D Sentences
    IoU overall· 2016-03-20
    0.474
    best: 0.807 (SOC (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.5· 2016-03-20
    0.348
    best: 0.851 (SOC (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.6· 2016-03-20
    0.236
    best: 0.827 (SOC (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.7· 2016-03-20
    0.133
    best: 0.767 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.8· 2016-03-20
    0.033
    best: 0.617 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonJ-HMDB
    AP· 2016-03-20
    0.178
    best: 0.45 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonJ-HMDB
    IoU mean· 2016-03-20
    0.528
    best: 0.725 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonJ-HMDB
    IoU overall· 2016-03-20
    0.546
    best: 0.737 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonJ-HMDB
    Precision@0.5· 2016-03-20
    0.633
    best: 0.972 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonJ-HMDB
    Precision@0.6· 2016-03-20
    0.35
    best: 0.917 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonJ-HMDB
    Precision@0.7· 2016-03-20
    0.085
    best: 0.714 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Referring Expression SegmentationonJ-HMDB
    Precision@0.8· 2016-03-20
    0.002
    best: 0.225 (SgMg (Video-Swin-B))
    SOTA
    Segmentation from Natural Language ExpressionsarXiv:1603.06180
  • Instance SegmentationonA2D Sentences
    Precision@0.9
    0
    best: 0.259 (SgMg (Video-Swin-B))
  • Instance SegmentationonJ-HMDB
    Precision@0.9
    0
    best: 0.4 (HINet)
  • Referring Expression SegmentationonA2D Sentences
    Precision@0.9
    0
    best: 0.259 (SgMg (Video-Swin-B))
  • Referring Expression SegmentationonJ-HMDB
    Precision@0.9
    0
    best: 0.4 (HINet)