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Models/TVOS

TVOS

Reported on 30 benchmarks across 3 tasks · 1 paper · 12 SOTA

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

Computer Vision30 results

VideoonDAVIS (no YouTube-VOS training)
D17 test (F)· 2020-04-15
67.4
best: 72.2 (TBD)
SOTA
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
VideoonDAVIS (no YouTube-VOS training)
D17 test (G)· 2020-04-15
63.1
best: 69.4 (TBD)
SOTA
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
VideoonDAVIS (no YouTube-VOS training)
D17 test (J)· 2020-04-15
58.8
best: 66.6 (TBD)
SOTA
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
VideoonDAVIS (no YouTube-VOS training)
FPS· 2020-04-15
37
best: 50.1 (TBD)
SOTA
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Video Object SegmentationonDAVIS (no YouTube-VOS training)
D17 test (F)· 2020-04-15
67.4
best: 72.2 (TBD)
SOTA
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Video Object SegmentationonDAVIS (no YouTube-VOS training)
D17 test (G)· 2020-04-15
63.1
best: 69.4 (TBD)
SOTA
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Video Object SegmentationonDAVIS (no YouTube-VOS training)
D17 test (J)· 2020-04-15
58.8
best: 66.6 (TBD)
SOTA
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Video Object SegmentationonDAVIS (no YouTube-VOS training)
FPS· 2020-04-15
37
best: 50.1 (TBD)
SOTA
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
D17 test (F)· 2020-04-15
67.4
best: 72.2 (TBD)
SOTA
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
D17 test (G)· 2020-04-15
63.1
best: 69.4 (TBD)
SOTA
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
D17 test (J)· 2020-04-15
58.8
best: 66.6 (TBD)
SOTA
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
FPS· 2020-04-15
37
best: 50.1 (TBD)
SOTA
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
VideoonDAVIS 2017 (val)
F-measure (Mean)· 2020-04-15
74.7
best: 93.4 (Cutie+ (base))
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
VideoonDAVIS 2017 (val)
J&F· 2020-04-15
72.3
best: 90.7 (SAM2)
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
VideoonDAVIS 2017 (val)
Jaccard (Mean)· 2020-04-15
69.9
best: 87.5 (Cutie+ (base))
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
VideoonDAVIS (no YouTube-VOS training)
D17 val (F)· 2020-04-15
74.7
best: 83.1 (HMMN)
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
VideoonDAVIS (no YouTube-VOS training)
D17 val (G)· 2020-04-15
72.3
best: 80.4 (HMMN)
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
VideoonDAVIS (no YouTube-VOS training)
D17 val (J)· 2020-04-15
69.9
best: 77.7 (HMMN)
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Video Object SegmentationonDAVIS 2017 (val)
F-measure (Mean)· 2020-04-15
74.7
best: 93.4 (Cutie+ (base))
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Video Object SegmentationonDAVIS 2017 (val)
J&F· 2020-04-15
72.3
best: 90.7 (SAM2)
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Video Object SegmentationonDAVIS 2017 (val)
Jaccard (Mean)· 2020-04-15
69.9
best: 87.5 (Cutie+ (base))
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Video Object SegmentationonDAVIS (no YouTube-VOS training)
D17 val (F)· 2020-04-15
74.7
best: 83.1 (HMMN)
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Video Object SegmentationonDAVIS (no YouTube-VOS training)
D17 val (G)· 2020-04-15
72.3
best: 80.4 (HMMN)
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Video Object SegmentationonDAVIS (no YouTube-VOS training)
D17 val (J)· 2020-04-15
69.9
best: 77.7 (HMMN)
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
F-measure (Mean)· 2020-04-15
74.7
best: 93.4 (Cutie+ (base))
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
J&F· 2020-04-15
72.3
best: 90.7 (SAM2)
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
Jaccard (Mean)· 2020-04-15
69.9
best: 87.5 (Cutie+ (base))
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
D17 val (F)· 2020-04-15
74.7
best: 83.1 (HMMN)
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
D17 val (G)· 2020-04-15
72.3
best: 80.4 (HMMN)
A Transductive Approach for Video Object Segmentation arXiv:2004.07193
Semi-Supervised Video Object SegmentationonDAVIS (no YouTube-VOS training)
D17 val (J)· 2020-04-15
69.9
best: 77.7 (HMMN)
A Transductive Approach for Video Object Segmentation arXiv:2004.07193