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Models/RVOS

RVOS

Reported on 57 benchmarks across 3 tasks · 1 paper · 9 SOTA

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

Computer Vision81 results

  • VideoonDAVIS 2017 (val)
    F-measure (Decay)· 2019-03-13
    28.2
    best: 85.3 (STCN)
    SOTA
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    F-measure (Decay)· 2019-03-13
    36.7
    best: 37.2 (RGMP)
    SOTA
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    Jaccard (Decay)· 2019-03-13
    35.7
    SOTA
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Decay)· 2019-03-13
    28.2
    best: 85.3 (STCN)
    SOTA
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Decay)· 2019-03-13
    36.7
    best: 37.2 (RGMP)
    SOTA
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Decay)· 2019-03-13
    35.7
    SOTA
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Decay)· 2019-03-13
    28.2
    best: 85.3 (STCN)
    SOTA
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Decay)· 2019-03-13
    36.7
    best: 37.2 (RGMP)
    SOTA
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Decay)· 2019-03-13
    35.7
    SOTA
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (val)
    F-measure (Mean)· 2019-03-13
    63.6
    best: 93.4 (Cutie+ (base))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (val)
    F-measure (Recall)· 2019-03-13
    73.2
    best: 94.6 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (val)
    J&F· 2019-03-13
    60.55
    best: 90.7 (SAM2)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (val)
    Jaccard (Decay)· 2019-03-13
    24.9
    best: 32.5 (MuG-W)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (val)
    Jaccard (Mean)· 2019-03-13
    57.5
    best: 87.5 (Cutie+ (base))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (val)
    Jaccard (Recall)· 2019-03-13
    65.2
    best: 91.4 (ISVOS (MS))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    F-measure (Mean)· 2019-03-13
    52.6
    best: 91.4 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    F-measure (Recall)· 2019-03-13
    61.7
    best: 89.7 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    J&F· 2019-03-13
    50.3
    best: 88.1 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    Jaccard (Mean)· 2019-03-13
    47.9
    best: 84.7 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    Jaccard (Recall)· 2019-03-13
    54.4
    best: 85.5 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonYouTube-VOS 2018
    F-Measure (Seen)· 2019-03-13
    67.2
    best: 91 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonYouTube-VOS 2018
    F-Measure (Unseen)· 2019-03-13
    51
    best: 90.2 (XMem (BL30K, MS))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonYouTube-VOS 2018
    Jaccard (Seen)· 2019-03-13
    63.6
    best: 86.6 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonYouTube-VOS 2018
    Overall· 2019-03-13
    56.8
    best: 87.5 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonYouTube-VOS 2018
    Speed (FPS)· 2019-03-13
    45.5
    best: 65.9 (FRTM)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    F-measure (Decay)· 2019-03-13
    1.8
    best: 37.2 (RGMP)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    F-measure (Mean)· 2019-03-13
    27.3
    best: 91.4 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    F-measure (Recall)· 2019-03-13
    24.8
    best: 89.7 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    J&F· 2019-03-13
    22.5
    best: 88.1 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    Jaccard (Decay)· 2019-03-13
    1.6
    best: 35.7
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    Jaccard (Mean)· 2019-03-13
    17.7
    best: 84.7 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (test-dev)
    Jaccard (Recall)· 2019-03-13
    16.2
    best: 85.5 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (val)
    F-measure (Mean)· 2019-03-13
    45.7
    best: 93.4 (Cutie+ (base))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (val)
    F-measure (Recall)· 2019-03-13
    46.4
    best: 94.6 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (val)
    J&F· 2019-03-13
    41.2
    best: 90.7 (SAM2)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (val)
    Jaccard (Mean)· 2019-03-13
    36.8
    best: 87.5 (Cutie+ (base))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • VideoonDAVIS 2017 (val)
    Jaccard (Recall)· 2019-03-13
    40.2
    best: 91.4 (ISVOS (MS))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Mean)· 2019-03-13
    63.6
    best: 93.4 (Cutie+ (base))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Recall)· 2019-03-13
    73.2
    best: 94.6 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (val)
    J&F· 2019-03-13
    60.55
    best: 90.7 (SAM2)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Decay)· 2019-03-13
    24.9
    best: 32.5 (MuG-W)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Mean)· 2019-03-13
    57.5
    best: 87.5 (Cutie+ (base))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Recall)· 2019-03-13
    65.2
    best: 91.4 (ISVOS (MS))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Mean)· 2019-03-13
    52.6
    best: 91.4 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Recall)· 2019-03-13
    61.7
    best: 89.7 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    J&F· 2019-03-13
    50.3
    best: 88.1 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Mean)· 2019-03-13
    47.9
    best: 84.7 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Recall)· 2019-03-13
    54.4
    best: 85.5 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonYouTube-VOS 2018
    F-Measure (Seen)· 2019-03-13
    67.2
    best: 91 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonYouTube-VOS 2018
    F-Measure (Unseen)· 2019-03-13
    51
    best: 90.2 (XMem (BL30K, MS))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonYouTube-VOS 2018
    Jaccard (Seen)· 2019-03-13
    63.6
    best: 86.6 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonYouTube-VOS 2018
    Overall· 2019-03-13
    56.8
    best: 87.5 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonYouTube-VOS 2018
    Speed (FPS)· 2019-03-13
    45.5
    best: 65.9 (FRTM)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Decay)· 2019-03-13
    1.8
    best: 37.2 (RGMP)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Mean)· 2019-03-13
    27.3
    best: 91.4 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Recall)· 2019-03-13
    24.8
    best: 89.7 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    J&F· 2019-03-13
    22.5
    best: 88.1 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Decay)· 2019-03-13
    1.6
    best: 35.7
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Mean)· 2019-03-13
    17.7
    best: 84.7 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Recall)· 2019-03-13
    16.2
    best: 85.5 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Mean)· 2019-03-13
    45.7
    best: 93.4 (Cutie+ (base))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Recall)· 2019-03-13
    46.4
    best: 94.6 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (val)
    J&F· 2019-03-13
    41.2
    best: 90.7 (SAM2)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Mean)· 2019-03-13
    36.8
    best: 87.5 (Cutie+ (base))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Recall)· 2019-03-13
    40.2
    best: 91.4 (ISVOS (MS))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Mean)· 2019-03-13
    63.6
    best: 93.4 (Cutie+ (base))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Recall)· 2019-03-13
    73.2
    best: 94.6 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    J&F· 2019-03-13
    60.55
    best: 90.7 (SAM2)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Decay)· 2019-03-13
    24.9
    best: 32.5 (MuG-W)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Mean)· 2019-03-13
    57.5
    best: 87.5 (Cutie+ (base))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Recall)· 2019-03-13
    65.2
    best: 91.4 (ISVOS (MS))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Mean)· 2019-03-13
    52.6
    best: 91.4 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Recall)· 2019-03-13
    61.7
    best: 89.7 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    J&F· 2019-03-13
    50.3
    best: 88.1 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Mean)· 2019-03-13
    47.9
    best: 84.7 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Recall)· 2019-03-13
    54.4
    best: 85.5 (STCN)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
    F-Measure (Seen)· 2019-03-13
    67.2
    best: 91 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
    F-Measure (Unseen)· 2019-03-13
    51
    best: 90.2 (XMem (BL30K, MS))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
    Jaccard (Seen)· 2019-03-13
    63.6
    best: 86.6 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
    Overall· 2019-03-13
    56.8
    best: 87.5 (Cutie+ (base, MEGA))
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612
  • Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
    Speed (FPS)· 2019-03-13
    45.5
    best: 65.9 (FRTM)
    RVOS: End-to-End Recurrent Network for Video Object SegmentationarXiv:1903.05612