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Models/Spatiotemporal CNN

Spatiotemporal CNN

Reported on 21 benchmarks across 3 tasks · 1 paper · 1 SOTA

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

Computer Vision21 results

Semi-Supervised Video Object SegmentationonYouTube
mIoU· 2019-04-04
0.796
SOTA
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
VideoonDAVIS 2017 (val)
F-measure (Mean)· 2019-04-04
64.6
best: 93.4 (Cutie+ (base))
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
VideoonDAVIS 2017 (val)
J&F· 2019-04-04
61.65
best: 90.7 (SAM2)
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
VideoonDAVIS 2017 (val)
Jaccard (Mean)· 2019-04-04
58.7
best: 87.5 (Cutie+ (base))
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
VideoonDAVIS 2016
F-measure (Mean)· 2019-04-04
83.8
best: 94.7 (SwinB-DeAOT-L)
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
VideoonDAVIS 2016
J&F· 2019-04-04
83.8
best: 93.4 (ISVOS (BL30K, MS))
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
VideoonDAVIS 2016
Jaccard (Mean)· 2019-04-04
83.8
best: 92.5 (ISVOS (BL30K, MS))
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
VideoonYouTube
mIoU· 2019-04-04
0.796
best: 0.821 (FEELVOS)
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Video Object SegmentationonDAVIS 2017 (val)
F-measure (Mean)· 2019-04-04
64.6
best: 93.4 (Cutie+ (base))
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Video Object SegmentationonDAVIS 2017 (val)
J&F· 2019-04-04
61.65
best: 90.7 (SAM2)
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Video Object SegmentationonDAVIS 2017 (val)
Jaccard (Mean)· 2019-04-04
58.7
best: 87.5 (Cutie+ (base))
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Video Object SegmentationonDAVIS 2016
F-measure (Mean)· 2019-04-04
83.8
best: 94.7 (SwinB-DeAOT-L)
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Video Object SegmentationonDAVIS 2016
J&F· 2019-04-04
83.8
best: 93.4 (ISVOS (BL30K, MS))
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Video Object SegmentationonDAVIS 2016
Jaccard (Mean)· 2019-04-04
83.8
best: 92.5 (ISVOS (BL30K, MS))
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Video Object SegmentationonYouTube
mIoU· 2019-04-04
0.796
best: 0.821 (FEELVOS)
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
F-measure (Mean)· 2019-04-04
64.6
best: 93.4 (Cutie+ (base))
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
J&F· 2019-04-04
61.65
best: 90.7 (SAM2)
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Semi-Supervised Video Object SegmentationonDAVIS 2017 (val)
Jaccard (Mean)· 2019-04-04
58.7
best: 87.5 (Cutie+ (base))
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Semi-Supervised Video Object SegmentationonDAVIS 2016
F-measure (Mean)· 2019-04-04
83.8
best: 94.7 (SwinB-DeAOT-L)
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Semi-Supervised Video Object SegmentationonDAVIS 2016
J&F· 2019-04-04
83.8
best: 93.4 (ISVOS (BL30K, MS))
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363
Semi-Supervised Video Object SegmentationonDAVIS 2016
Jaccard (Mean)· 2019-04-04
83.8
best: 92.5 (ISVOS (BL30K, MS))
Spatiotemporal CNN for Video Object Segmentation arXiv:1904.02363