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Models/TarViS

TarViS

Reported on 9 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 Vision9 results

  • VideoonDAVIS 2017 (val)
    F-measure (Mean)· uses extra data· 2023-01-06
    88.5
    best: 93.4 (Cutie+ (base))
    TarViS: A Unified Approach for Target-based Video SegmentationarXiv:2301.02657
  • VideoonDAVIS 2017 (val)
    J&F· uses extra data· 2023-01-06
    85.3
    best: 90.7 (SAM2)
    TarViS: A Unified Approach for Target-based Video SegmentationarXiv:2301.02657
  • VideoonDAVIS 2017 (val)
    Jaccard (Mean)· uses extra data· 2023-01-06
    81.7
    best: 87.5 (Cutie+ (base))
    TarViS: A Unified Approach for Target-based Video SegmentationarXiv:2301.02657
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Mean)· uses extra data· 2023-01-06
    88.5
    best: 93.4 (Cutie+ (base))
    TarViS: A Unified Approach for Target-based Video SegmentationarXiv:2301.02657
  • Video Object SegmentationonDAVIS 2017 (val)
    J&F· uses extra data· 2023-01-06
    85.3
    best: 90.7 (SAM2)
    TarViS: A Unified Approach for Target-based Video SegmentationarXiv:2301.02657
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Mean)· uses extra data· 2023-01-06
    81.7
    best: 87.5 (Cutie+ (base))
    TarViS: A Unified Approach for Target-based Video SegmentationarXiv:2301.02657
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Mean)· uses extra data· 2023-01-06
    88.5
    best: 93.4 (Cutie+ (base))
    TarViS: A Unified Approach for Target-based Video SegmentationarXiv:2301.02657
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    J&F· uses extra data· 2023-01-06
    85.3
    best: 90.7 (SAM2)
    TarViS: A Unified Approach for Target-based Video SegmentationarXiv:2301.02657
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Mean)· uses extra data· 2023-01-06
    81.7
    best: 87.5 (Cutie+ (base))
    TarViS: A Unified Approach for Target-based Video SegmentationarXiv:2301.02657