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Models/STM

STM

Reported on 106 benchmarks across 4 tasks · 3 papers · 68 SOTA

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

Computer Vision105 results

  • VideoonDAVIS 2017 (val)
    F-measure· 2019-04-01
    84.3
    best: 92.6 (XMem (BLK30K, MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (val)
    Jaccard· 2019-04-01
    79.2
    best: 86.3 (XMem (BLK30K, MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (val)
    F-measure (Mean)· uses extra data· 2019-04-01
    84.3
    best: 93.4 (Cutie+ (base))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (val)
    F-measure (Recall)· uses extra data· 2019-04-01
    91.8
    best: 94.6 (STCN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (val)
    J&F· uses extra data· 2019-04-01
    81.75
    best: 90.7 (SAM2)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (val)
    Jaccard (Mean)· uses extra data· 2019-04-01
    79.2
    best: 87.5 (Cutie+ (base))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (val)
    Jaccard (Recall)· uses extra data· 2019-04-01
    88.7
    best: 91.4 (ISVOS (MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2016
    F-measure (Mean)· uses extra data· 2019-04-01
    90.1
    best: 94.7 (SwinB-DeAOT-L)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2016
    J&F· uses extra data· 2019-04-01
    89.4
    best: 93.4 (ISVOS (BL30K, MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2016
    Jaccard (Mean)· uses extra data· 2019-04-01
    88.7
    best: 92.5 (ISVOS (BL30K, MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2016
    Jaccard (Recall)· uses extra data· 2019-04-01
    97.4
    best: 98.1 (STCN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (test-dev)
    J&F· uses extra data· 2019-04-01
    72.2
    best: 88.1 (Cutie+ (base, MEGA))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (test-dev)
    Jaccard (Mean)· uses extra data· 2019-04-01
    69.3
    best: 84.7 (Cutie+ (base, MEGA))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (test-dev)
    Jaccard (Recall)· uses extra data· 2019-04-01
    78
    best: 85.5 (STCN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS (no YouTube-VOS training)
    D16 val (F)· 2019-04-01
    88.1
    best: 90.6 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS (no YouTube-VOS training)
    D16 val (G)· 2019-04-01
    86.5
    best: 89.4 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS (no YouTube-VOS training)
    D16 val (J)· 2019-04-01
    84.8
    best: 88.2 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS (no YouTube-VOS training)
    D17 val (F)· 2019-04-01
    74
    best: 83.1 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS (no YouTube-VOS training)
    D17 val (G)· 2019-04-01
    71.6
    best: 80.4 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS (no YouTube-VOS training)
    D17 val (J)· 2019-04-01
    69.2
    best: 77.7 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS (no YouTube-VOS training)
    FPS· 2019-04-01
    6.25
    best: 50.1 (TBD)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonYouTube-VOS 2018
    Overall· 2019-04-01
    68.2
    best: 87.5 (Cutie+ (base, MEGA))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017
    AUC-J&F· uses extra data· 2019-04-01
    0.803
    best: 0.879 (MiVOS)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017
    J&F@60s· uses extra data· 2019-04-01
    0.848
    best: 0.885 (MiVOS)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure· 2019-04-01
    84.3
    best: 92.6 (XMem (BLK30K, MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard· 2019-04-01
    79.2
    best: 86.3 (XMem (BLK30K, MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Mean)· uses extra data· 2019-04-01
    84.3
    best: 93.4 (Cutie+ (base))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Recall)· uses extra data· 2019-04-01
    91.8
    best: 94.6 (STCN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (val)
    J&F· uses extra data· 2019-04-01
    81.75
    best: 90.7 (SAM2)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Mean)· uses extra data· 2019-04-01
    79.2
    best: 87.5 (Cutie+ (base))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Recall)· uses extra data· 2019-04-01
    88.7
    best: 91.4 (ISVOS (MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2016
    F-measure (Mean)· uses extra data· 2019-04-01
    90.1
    best: 94.7 (SwinB-DeAOT-L)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2016
    J&F· uses extra data· 2019-04-01
    89.4
    best: 93.4 (ISVOS (BL30K, MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2016
    Jaccard (Mean)· uses extra data· 2019-04-01
    88.7
    best: 92.5 (ISVOS (BL30K, MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2016
    Jaccard (Recall)· uses extra data· 2019-04-01
    97.4
    best: 98.1 (STCN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    J&F· uses extra data· 2019-04-01
    72.2
    best: 88.1 (Cutie+ (base, MEGA))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Mean)· uses extra data· 2019-04-01
    69.3
    best: 84.7 (Cutie+ (base, MEGA))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Recall)· uses extra data· 2019-04-01
    78
    best: 85.5 (STCN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS (no YouTube-VOS training)
    D16 val (F)· 2019-04-01
    88.1
    best: 90.6 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS (no YouTube-VOS training)
    D16 val (G)· 2019-04-01
    86.5
    best: 89.4 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS (no YouTube-VOS training)
    D16 val (J)· 2019-04-01
    84.8
    best: 88.2 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS (no YouTube-VOS training)
    D17 val (F)· 2019-04-01
    74
    best: 83.1 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS (no YouTube-VOS training)
    D17 val (G)· 2019-04-01
    71.6
    best: 80.4 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS (no YouTube-VOS training)
    D17 val (J)· 2019-04-01
    69.2
    best: 77.7 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS (no YouTube-VOS training)
    FPS· 2019-04-01
    6.25
    best: 50.1 (TBD)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonYouTube-VOS 2018
    Overall· 2019-04-01
    68.2
    best: 87.5 (Cutie+ (base, MEGA))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017
    AUC-J&F· uses extra data· 2019-04-01
    0.803
    best: 0.879 (MiVOS)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017
    J&F@60s· uses extra data· 2019-04-01
    0.848
    best: 0.885 (MiVOS)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Mean)· uses extra data· 2019-04-01
    84.3
    best: 93.4 (Cutie+ (base))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Recall)· uses extra data· 2019-04-01
    91.8
    best: 94.6 (STCN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    J&F· uses extra data· 2019-04-01
    81.75
    best: 90.7 (SAM2)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Mean)· uses extra data· 2019-04-01
    79.2
    best: 87.5 (Cutie+ (base))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Recall)· uses extra data· 2019-04-01
    88.7
    best: 91.4 (ISVOS (MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    F-measure (Mean)· uses extra data· 2019-04-01
    90.1
    best: 94.7 (SwinB-DeAOT-L)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    J&F· uses extra data· 2019-04-01
    89.4
    best: 93.4 (ISVOS (BL30K, MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    Jaccard (Mean)· uses extra data· 2019-04-01
    88.7
    best: 92.5 (ISVOS (BL30K, MS))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    Jaccard (Recall)· uses extra data· 2019-04-01
    97.4
    best: 98.1 (STCN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    J&F· uses extra data· 2019-04-01
    72.2
    best: 88.1 (Cutie+ (base, MEGA))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Mean)· uses extra data· 2019-04-01
    69.3
    best: 84.7 (Cutie+ (base, MEGA))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Recall)· uses extra data· 2019-04-01
    78
    best: 85.5 (STCN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
    D16 val (F)· 2019-04-01
    88.1
    best: 90.6 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
    D16 val (G)· 2019-04-01
    86.5
    best: 89.4 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
    D16 val (J)· 2019-04-01
    84.8
    best: 88.2 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
    D17 val (F)· 2019-04-01
    74
    best: 83.1 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
    D17 val (G)· 2019-04-01
    71.6
    best: 80.4 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
    D17 val (J)· 2019-04-01
    69.2
    best: 77.7 (HMMN)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
    FPS· 2019-04-01
    6.25
    best: 50.1 (TBD)
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
    Overall· 2019-04-01
    68.2
    best: 87.5 (Cutie+ (base, MEGA))
    SOTA
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonYouTube-VOS 2018
    F-Measure (Seen)· 2022-08-01
    84.2
    best: 91 (Cutie+ (base, MEGA))
    BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object SegmentationarXiv:2208.01159
  • VideoonYouTube-VOS 2018
    F-Measure (Unseen)· 2022-08-01
    80.9
    best: 90.2 (XMem (BL30K, MS))
    BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object SegmentationarXiv:2208.01159
  • VideoonYouTube-VOS 2018
    Jaccard (Seen)· 2022-08-01
    79.7
    best: 86.6 (Cutie+ (base, MEGA))
    BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object SegmentationarXiv:2208.01159
  • VideoonYouTube-VOS 2018
    Jaccard (Unseen)· 2022-08-01
    72.8
    best: 82.2 (Cutie+ (base, MEGA))
    BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object SegmentationarXiv:2208.01159
  • VideoonYouTube-VOS 2018
    Mean Jaccard & F-Measure· 2022-08-01
    79.4
    best: 86.9 (XMem (BL30K, MS))
    BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object SegmentationarXiv:2208.01159
  • Video Object SegmentationonYouTube-VOS 2018
    F-Measure (Seen)· 2022-08-01
    84.2
    best: 91 (Cutie+ (base, MEGA))
    BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object SegmentationarXiv:2208.01159
  • Video Object SegmentationonYouTube-VOS 2018
    F-Measure (Unseen)· 2022-08-01
    80.9
    best: 90.2 (XMem (BL30K, MS))
    BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object SegmentationarXiv:2208.01159
  • Video Object SegmentationonYouTube-VOS 2018
    Jaccard (Seen)· 2022-08-01
    79.7
    best: 86.6 (Cutie+ (base, MEGA))
    BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object SegmentationarXiv:2208.01159
  • Video Object SegmentationonYouTube-VOS 2018
    Jaccard (Unseen)· 2022-08-01
    72.8
    best: 82.2 (Cutie+ (base, MEGA))
    BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object SegmentationarXiv:2208.01159
  • Video Object SegmentationonYouTube-VOS 2018
    Mean Jaccard & F-Measure· 2022-08-01
    79.4
    best: 86.9 (XMem (BL30K, MS))
    BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object SegmentationarXiv:2208.01159
  • VideoonDAVIS 2017 (val)
    F-measure (Decay)· uses extra data· 2019-04-01
    10.5
    best: 85.3 (STCN)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (val)
    Jaccard (Decay)· uses extra data· 2019-04-01
    8
    best: 32.5 (MuG-W)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2016
    F-measure (Decay)· uses extra data· 2019-04-01
    4.2
    best: 27.2 (OFL)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2016
    F-measure (Recall)· uses extra data· 2019-04-01
    95.2
    best: 97.1 (STCN)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2016
    Jaccard (Decay)· uses extra data· 2019-04-01
    5
    best: 28.9 (BVS)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (test-dev)
    F-measure (Decay)· uses extra data· 2019-04-01
    17.5
    best: 37.2 (RGMP)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (test-dev)
    F-measure (Mean)· uses extra data· 2019-04-01
    75.2
    best: 91.4 (Cutie+ (base, MEGA))
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (test-dev)
    F-measure (Recall)· uses extra data· 2019-04-01
    83
    best: 89.7 (STCN)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • VideoonDAVIS 2017 (test-dev)
    Jaccard (Decay)· uses extra data· 2019-04-01
    16.9
    best: 35.7 (RVOS)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Decay)· uses extra data· 2019-04-01
    10.5
    best: 85.3 (STCN)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Decay)· uses extra data· 2019-04-01
    8
    best: 32.5 (MuG-W)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2016
    F-measure (Decay)· uses extra data· 2019-04-01
    4.2
    best: 27.2 (OFL)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2016
    F-measure (Recall)· uses extra data· 2019-04-01
    95.2
    best: 97.1 (STCN)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2016
    Jaccard (Decay)· uses extra data· 2019-04-01
    5
    best: 28.9 (BVS)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Decay)· uses extra data· 2019-04-01
    17.5
    best: 37.2 (RGMP)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Mean)· uses extra data· 2019-04-01
    75.2
    best: 91.4 (Cutie+ (base, MEGA))
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Recall)· uses extra data· 2019-04-01
    83
    best: 89.7 (STCN)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Decay)· uses extra data· 2019-04-01
    16.9
    best: 35.7 (RVOS)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    F-measure (Decay)· uses extra data· 2019-04-01
    10.5
    best: 85.3 (STCN)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
    Jaccard (Decay)· uses extra data· 2019-04-01
    8
    best: 32.5 (MuG-W)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    F-measure (Decay)· uses extra data· 2019-04-01
    4.2
    best: 27.2 (OFL)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    F-measure (Recall)· uses extra data· 2019-04-01
    95.2
    best: 97.1 (STCN)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2016
    Jaccard (Decay)· uses extra data· 2019-04-01
    5
    best: 28.9 (BVS)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Decay)· uses extra data· 2019-04-01
    17.5
    best: 37.2 (RGMP)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Mean)· uses extra data· 2019-04-01
    75.2
    best: 91.4 (Cutie+ (base, MEGA))
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    F-measure (Recall)· uses extra data· 2019-04-01
    83
    best: 89.7 (STCN)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607
  • Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
    Jaccard (Decay)· uses extra data· 2019-04-01
    16.9
    best: 35.7 (RVOS)
    Video Object Segmentation using Space-Time Memory NetworksarXiv:1904.00607

Natural Language Processing1 result

  • Named Entity Recognition (NER)onNCBI-disease
    F1· 2019-09-25
    88.6
    best: 89.71 (BioBERT)
    Learning A Unified Named Entity Tagger From Multiple Partially Annotated Corpora For Efficient AdaptationarXiv:1909.11535