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Models/SWEM (val)

SWEM (val)

Reported on 12 benchmarks across 3 tasks · 1 paper

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

Computer Vision12 results

  • VideoonDAVIS 2016
    F-measure (Mean)· 2022-08-22
    89
    best: 94.7 (SwinB-DeAOT-L)
    SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationarXiv:2208.10128
  • VideoonDAVIS 2016
    J&F· 2022-08-22
    88.1
    best: 93.4 (ISVOS (BL30K, MS))
    SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationarXiv:2208.10128
  • VideoonDAVIS 2016
    Jaccard (Mean)· 2022-08-22
    87.3
    best: 92.5 (ISVOS (BL30K, MS))
    SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationarXiv:2208.10128
  • VideoonDAVIS 2016
    Speed (FPS)· 2022-08-22
    36
    best: 100.1 (MobileVOS (BL30K))
    SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationarXiv:2208.10128
  • Video Object SegmentationonDAVIS 2016
    F-measure (Mean)· 2022-08-22
    89
    best: 94.7 (SwinB-DeAOT-L)
    SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationarXiv:2208.10128
  • Video Object SegmentationonDAVIS 2016
    J&F· 2022-08-22
    88.1
    best: 93.4 (ISVOS (BL30K, MS))
    SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationarXiv:2208.10128
  • Video Object SegmentationonDAVIS 2016
    Jaccard (Mean)· 2022-08-22
    87.3
    best: 92.5 (ISVOS (BL30K, MS))
    SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationarXiv:2208.10128
  • Video Object SegmentationonDAVIS 2016
    Speed (FPS)· 2022-08-22
    36
    best: 100.1 (MobileVOS (BL30K))
    SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationarXiv:2208.10128
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    F-measure (Mean)· 2022-08-22
    89
    best: 94.7 (SwinB-DeAOT-L)
    SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationarXiv:2208.10128
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    J&F· 2022-08-22
    88.1
    best: 93.4 (ISVOS (BL30K, MS))
    SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationarXiv:2208.10128
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    Jaccard (Mean)· 2022-08-22
    87.3
    best: 92.5 (ISVOS (BL30K, MS))
    SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationarXiv:2208.10128
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    Speed (FPS)· 2022-08-22
    36
    best: 100.1 (MobileVOS (BL30K))
    SWEM: Towards Real-Time Video Object Segmentation with Sequential Weighted Expectation-MaximizationarXiv:2208.10128