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Models/OSVOS

OSVOS

Reported on 87 benchmarks across 5 tasks · 1 paper · 87 SOTA

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

Computer Vision90 results

VideoonDAVIS 2017 (val)
F-measure (Decay)· 2016-11-16
27
best: 85.3 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2017 (val)
F-measure (Mean)· 2016-11-16
63.9
best: 93.4 (Cutie+ (base))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2017 (val)
F-measure (Recall)· 2016-11-16
73.8
best: 94.6 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2017 (val)
J&F· 2016-11-16
60.25
best: 90.7 (SAM2)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2017 (val)
Jaccard (Decay)· 2016-11-16
26.1
best: 32.5 (MuG-W)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2017 (val)
Jaccard (Mean)· 2016-11-16
56.6
best: 87.5 (Cutie+ (base))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2017 (val)
Jaccard (Recall)· 2016-11-16
63.8
best: 91.4 (ISVOS (MS))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2016
F-measure (Decay)· 2016-11-16
15
best: 27.2 (OFL)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2016
F-measure (Mean)· 2016-11-16
80.6
best: 94.7 (SwinB-DeAOT-L)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2016
F-measure (Recall)· 2016-11-16
92.6
best: 97.1 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2016
J&F· 2016-11-16
80.2
best: 93.4 (ISVOS (BL30K, MS))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2016
Jaccard (Decay)· 2016-11-16
14.9
best: 28.9 (BVS)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2016
Jaccard (Mean)· 2016-11-16
79.8
best: 92.5 (ISVOS (BL30K, MS))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2016
Jaccard (Recall)· 2016-11-16
93.6
best: 98.1 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonYouTube
mIoU· 2016-11-16
0.783
best: 0.821 (FEELVOS)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2017 (test-dev)
F-measure (Decay)· 2016-11-16
19.8
best: 37.2 (RGMP)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2017 (test-dev)
F-measure (Recall)· 2016-11-16
59.7
best: 89.7 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2017 (test-dev)
J&F· 2016-11-16
50.9
best: 88.1 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2017 (test-dev)
Jaccard (Decay)· 2016-11-16
19.2
best: 35.7 (RVOS)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2017 (test-dev)
Jaccard (Mean)· 2016-11-16
47
best: 84.7 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonDAVIS 2017 (test-dev)
Jaccard (Recall)· 2016-11-16
52.1
best: 85.5 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonYouTube-VOS 2018
F-Measure (Seen)· 2016-11-16
60.5
best: 91 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonYouTube-VOS 2018
F-Measure (Unseen)· 2016-11-16
60.7
best: 90.2 (XMem (BL30K, MS))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonYouTube-VOS 2018
Jaccard (Seen)· 2016-11-16
59.8
best: 86.6 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonYouTube-VOS 2018
Jaccard (Unseen)· 2016-11-16
54.2
best: 82.2 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonYouTube-VOS 2018
Overall· 2016-11-16
58.8
best: 87.5 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonYouTube-VOS 2018
Speed (FPS)· 2016-11-16
0.1
best: 65.9 (FRTM)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Object TrackingonYouTube-VOS 2018
F-Measure (Seen)· 2016-11-16
60.5
best: 86.7 (TransVOS)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Object TrackingonYouTube-VOS 2018
F-Measure (Unseen)· 2016-11-16
60.7
best: 83.4 (TransVOS)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Object TrackingonYouTube-VOS 2018
O (Average of Measures)· 2016-11-16
58.8
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (val)
F-measure (Decay)· 2016-11-16
27
best: 85.3 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (val)
F-measure (Mean)· 2016-11-16
63.9
best: 93.4 (Cutie+ (base))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (val)
F-measure (Recall)· 2016-11-16
73.8
best: 94.6 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (val)
J&F· 2016-11-16
60.25
best: 90.7 (SAM2)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (val)
Jaccard (Decay)· 2016-11-16
26.1
best: 32.5 (MuG-W)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (val)
Jaccard (Mean)· 2016-11-16
56.6
best: 87.5 (Cutie+ (base))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (val)
Jaccard (Recall)· 2016-11-16
63.8
best: 91.4 (ISVOS (MS))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2016
F-measure (Decay)· 2016-11-16
15
best: 27.2 (OFL)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2016
F-measure (Mean)· 2016-11-16
80.6
best: 94.7 (SwinB-DeAOT-L)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2016
F-measure (Recall)· 2016-11-16
92.6
best: 97.1 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2016
J&F· 2016-11-16
80.2
best: 93.4 (ISVOS (BL30K, MS))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2016
Jaccard (Decay)· 2016-11-16
14.9
best: 28.9 (BVS)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2016
Jaccard (Mean)· 2016-11-16
79.8
best: 92.5 (ISVOS (BL30K, MS))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2016
Jaccard (Recall)· 2016-11-16
93.6
best: 98.1 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonYouTube
mIoU· 2016-11-16
0.783
best: 0.821 (FEELVOS)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (test-dev)
F-measure (Decay)· 2016-11-16
19.8
best: 37.2 (RGMP)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (test-dev)
F-measure (Recall)· 2016-11-16
59.7
best: 89.7 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (test-dev)
J&F· 2016-11-16
50.9
best: 88.1 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (test-dev)
Jaccard (Decay)· 2016-11-16
19.2
best: 35.7 (RVOS)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (test-dev)
Jaccard (Mean)· 2016-11-16
47
best: 84.7 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonDAVIS 2017 (test-dev)
Jaccard (Recall)· 2016-11-16
52.1
best: 85.5 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonYouTube-VOS 2018
F-Measure (Seen)· 2016-11-16
60.5
best: 91 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonYouTube-VOS 2018
F-Measure (Unseen)· 2016-11-16
60.7
best: 90.2 (XMem (BL30K, MS))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonYouTube-VOS 2018
Jaccard (Seen)· 2016-11-16
59.8
best: 86.6 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonYouTube-VOS 2018
Jaccard (Unseen)· 2016-11-16
54.2
best: 82.2 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonYouTube-VOS 2018
Overall· 2016-11-16
58.8
best: 87.5 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonYouTube-VOS 2018
Speed (FPS)· 2016-11-16
0.1
best: 65.9 (FRTM)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
F-measure (Decay)· 2016-11-16
27
best: 85.3 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
F-measure (Mean)· 2016-11-16
63.9
best: 93.4 (Cutie+ (base))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
F-measure (Recall)· 2016-11-16
73.8
best: 94.6 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
J&F· 2016-11-16
60.25
best: 90.7 (SAM2)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
Jaccard (Decay)· 2016-11-16
26.1
best: 32.5 (MuG-W)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
Jaccard (Mean)· 2016-11-16
56.6
best: 87.5 (Cutie+ (base))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
Jaccard (Recall)· 2016-11-16
63.8
best: 91.4 (ISVOS (MS))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2016
F-measure (Decay)· 2016-11-16
15
best: 27.2 (OFL)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2016
F-measure (Mean)· 2016-11-16
80.6
best: 94.7 (SwinB-DeAOT-L)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2016
F-measure (Recall)· 2016-11-16
92.6
best: 97.1 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2016
J&F· 2016-11-16
80.2
best: 93.4 (ISVOS (BL30K, MS))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2016
Jaccard (Decay)· 2016-11-16
14.9
best: 28.9 (BVS)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2016
Jaccard (Mean)· 2016-11-16
79.8
best: 92.5 (ISVOS (BL30K, MS))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2016
Jaccard (Recall)· 2016-11-16
93.6
best: 98.1 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonYouTube
mIoU· 2016-11-16
0.783
best: 0.796 (Spatiotemporal CNN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
F-measure (Decay)· 2016-11-16
19.8
best: 37.2 (RGMP)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
F-measure (Recall)· 2016-11-16
59.7
best: 89.7 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
J&F· 2016-11-16
50.9
best: 88.1 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
Jaccard (Decay)· 2016-11-16
19.2
best: 35.7 (RVOS)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
Jaccard (Mean)· 2016-11-16
47
best: 84.7 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonDAVIS 2017 (test-dev)
Jaccard (Recall)· 2016-11-16
52.1
best: 85.5 (STCN)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
F-Measure (Seen)· 2016-11-16
60.5
best: 91 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
F-Measure (Unseen)· 2016-11-16
60.7
best: 90.2 (XMem (BL30K, MS))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
Jaccard (Seen)· 2016-11-16
59.8
best: 86.6 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
Jaccard (Unseen)· 2016-11-16
54.2
best: 82.2 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
Overall· 2016-11-16
58.8
best: 87.5 (Cutie+ (base, MEGA))
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
Speed (FPS)· 2016-11-16
0.1
best: 65.9 (FRTM)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Visual Object TrackingonYouTube-VOS 2018
F-Measure (Seen)· 2016-11-16
60.5
best: 86.7 (TransVOS)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Visual Object TrackingonYouTube-VOS 2018
F-Measure (Unseen)· 2016-11-16
60.7
best: 83.4 (TransVOS)
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
Visual Object TrackingonYouTube-VOS 2018
O (Average of Measures)· 2016-11-16
58.8
SOTA
One-Shot Video Object Segmentation arXiv:1611.05198
VideoonYouTube-VOS 2018
F-Measure (Seen)· 2016-11-16
60.5
best: 91 (Cutie+ (base, MEGA))
One-Shot Video Object Segmentation arXiv:1611.05198
Video Object SegmentationonYouTube-VOS 2018
F-Measure (Seen)· 2016-11-16
60.5
best: 91 (Cutie+ (base, MEGA))
One-Shot Video Object Segmentation arXiv:1611.05198
Semi-Supervised Video Object SegmentationonYouTube-VOS 2018
F-Measure (Seen)· 2016-11-16
60.5
best: 91 (Cutie+ (base, MEGA))
One-Shot Video Object Segmentation arXiv:1611.05198