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Models/RAVOS

RAVOS

Reported on 39 benchmarks across 3 tasks · 1 paper · 3 SOTA

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

Computer Vision39 results

VideoonDAVIS 2016
Speed (FPS)· uses extra data· 2022-07-21
58
best: 100.1 (MobileVOS (BL30K))
SOTA
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonDAVIS 2016
Speed (FPS)· uses extra data· 2022-07-21
58
best: 100.1 (MobileVOS (BL30K))
SOTA
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonDAVIS 2016
Speed (FPS)· uses extra data· 2022-07-21
58
best: 100.1 (MobileVOS (BL30K))
SOTA
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
VideoonDAVIS 2017 (val)
F-measure (Mean)· uses extra data· 2022-07-21
89.3
best: 93.4 (Cutie+ (base))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
VideoonDAVIS 2017 (val)
J&F· uses extra data· 2022-07-21
86.1
best: 90.7 (SAM2)
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
VideoonDAVIS 2017 (val)
Jaccard (Mean)· uses extra data· 2022-07-21
82.9
best: 87.5 (Cutie+ (base))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
VideoonDAVIS 2016
F-measure (Mean)· uses extra data· 2022-07-21
92.6
best: 94.7 (SwinB-DeAOT-L)
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
VideoonDAVIS 2016
J&F· uses extra data· 2022-07-21
91.7
best: 93.4 (ISVOS (BL30K, MS))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
VideoonDAVIS 2016
Jaccard (Mean)· uses extra data· 2022-07-21
90.8
best: 92.5 (ISVOS (BL30K, MS))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
VideoonYouTube-VOS 2018
F-Measure (Seen)· uses extra data· 2022-07-21
87.8
best: 91 (Cutie+ (base, MEGA))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
VideoonYouTube-VOS 2018
F-Measure (Unseen)· uses extra data· 2022-07-21
87.4
best: 90.2 (XMem (BL30K, MS))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
VideoonYouTube-VOS 2018
Jaccard (Seen)· uses extra data· 2022-07-21
83.1
best: 86.6 (Cutie+ (base, MEGA))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
VideoonYouTube-VOS 2018
Jaccard (Unseen)· uses extra data· 2022-07-21
79.1
best: 82.2 (Cutie+ (base, MEGA))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
VideoonYouTube-VOS 2018
Overall· uses extra data· 2022-07-21
84.4
best: 87.5 (Cutie+ (base, MEGA))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
VideoonYouTube-VOS 2018
Speed (FPS)· uses extra data· 2022-07-21
23
best: 65.9 (FRTM)
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonDAVIS 2017 (val)
F-measure (Mean)· uses extra data· 2022-07-21
89.3
best: 93.4 (Cutie+ (base))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonDAVIS 2017 (val)
J&F· uses extra data· 2022-07-21
86.1
best: 90.7 (SAM2)
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonDAVIS 2017 (val)
Jaccard (Mean)· uses extra data· 2022-07-21
82.9
best: 87.5 (Cutie+ (base))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonDAVIS 2016
F-measure (Mean)· uses extra data· 2022-07-21
92.6
best: 94.7 (SwinB-DeAOT-L)
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonDAVIS 2016
J&F· uses extra data· 2022-07-21
91.7
best: 93.4 (ISVOS (BL30K, MS))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonDAVIS 2016
Jaccard (Mean)· uses extra data· 2022-07-21
90.8
best: 92.5 (ISVOS (BL30K, MS))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonYouTube-VOS 2018
F-Measure (Seen)· uses extra data· 2022-07-21
87.8
best: 91 (Cutie+ (base, MEGA))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonYouTube-VOS 2018
F-Measure (Unseen)· uses extra data· 2022-07-21
87.4
best: 90.2 (XMem (BL30K, MS))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonYouTube-VOS 2018
Jaccard (Seen)· uses extra data· 2022-07-21
83.1
best: 86.6 (Cutie+ (base, MEGA))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonYouTube-VOS 2018
Jaccard (Unseen)· uses extra data· 2022-07-21
79.1
best: 82.2 (Cutie+ (base, MEGA))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonYouTube-VOS 2018
Overall· uses extra data· 2022-07-21
84.4
best: 87.5 (Cutie+ (base, MEGA))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Video Object SegmentationonYouTube-VOS 2018
Speed (FPS)· uses extra data· 2022-07-21
23
best: 65.9 (FRTM)
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
F-measure (Mean)· uses extra data· 2022-07-21
89.3
best: 93.4 (Cutie+ (base))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
J&F· uses extra data· 2022-07-21
86.1
best: 90.7 (SAM2)
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
Jaccard (Mean)· uses extra data· 2022-07-21
82.9
best: 87.5 (Cutie+ (base))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonDAVIS 2016
F-measure (Mean)· uses extra data· 2022-07-21
92.6
best: 94.7 (SwinB-DeAOT-L)
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonDAVIS 2016
J&F· uses extra data· 2022-07-21
91.7
best: 93.4 (ISVOS (BL30K, MS))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonDAVIS 2016
Jaccard (Mean)· uses extra data· 2022-07-21
90.8
best: 92.5 (ISVOS (BL30K, MS))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
F-Measure (Seen)· uses extra data· 2022-07-21
87.8
best: 91 (Cutie+ (base, MEGA))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
F-Measure (Unseen)· uses extra data· 2022-07-21
87.4
best: 90.2 (XMem (BL30K, MS))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
Jaccard (Seen)· uses extra data· 2022-07-21
83.1
best: 86.6 (Cutie+ (base, MEGA))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
Jaccard (Unseen)· uses extra data· 2022-07-21
79.1
best: 82.2 (Cutie+ (base, MEGA))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
Overall· uses extra data· 2022-07-21
84.4
best: 87.5 (Cutie+ (base, MEGA))
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
Speed (FPS)· uses extra data· 2022-07-21
23
best: 65.9 (FRTM)
Region Aware Video Object Segmentation with Deep Motion Modeling arXiv:2207.10258