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Models/COSNet

COSNet

Reported on 22 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.

Medical12 results

Medical Image SegmentationonSUN-SEG-Easy (Unseen)
Dice· 2020-01-19
0.596
best: 0.9 (YOLO-SAM 2)
SOTA
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
S measure· 2020-01-19
0.654
best: 0.9 (YOLO-SAM 2)
SOTA
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
mean E-measure· 2020-01-19
0.6
best: 93.8 (YOLO-SAM 2)
SOTA
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
mean F-measure· 2020-01-19
0.496
best: 93.8 (YOLO-SAM 2)
SOTA
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
weighted F-measure· 2020-01-19
0.431
best: 0.794 (SALI)
SOTA
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
Dice· 2020-01-19
0.606
best: 0.902 (YOLO-SAM 2)
SOTA
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
S-Measure· 2020-01-19
0.67
best: 0.894 (YOLO-SAM 2)
SOTA
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
mean E-measure· 2020-01-19
0.627
best: 0.941 (YOLO-SAM 2)
SOTA
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
mean F-measure· 2020-01-19
0.506
best: 0.932 (YOLO-SAM 2)
SOTA
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
weighted F-measure· 2020-01-19
0.443
best: 0.79 (SALI)
SOTA
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Medical Image SegmentationonSUN-SEG-Easy (Unseen)
Sensitivity· 2020-01-19
0.359
best: 83.7 (YOLO-SAM 2)
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Medical Image SegmentationonSUN-SEG-Hard (Unseen)
Sensitivity· 2020-01-19
0.38
best: 0.852 (YOLO-SAM 2)
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810

Computer Vision10 results

VideoonFBMS test
J· 2020-01-19
75.6
best: 84.7 (FakeFlow)
SOTA
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Video Object SegmentationonFBMS test
J· 2020-01-19
75.6
best: 84.7 (FakeFlow)
SOTA
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
VideoonDAVIS 2016 val
F· 2020-01-19
79.4
best: 90.2 (DEVA (DIS))
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
VideoonDAVIS 2016 val
G· 2020-01-19
80
best: 88.9 (GSANet)
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
VideoonDAVIS 2016 val
J· 2020-01-19
80.5
best: 88.3 (GSANet)
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
VideoonYouTube-Objects
J· 2020-01-19
70.5
best: 75.1 (FakeFlow)
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Video Object SegmentationonDAVIS 2016 val
F· 2020-01-19
79.4
best: 90.2 (DEVA (DIS))
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Video Object SegmentationonDAVIS 2016 val
G· 2020-01-19
80
best: 88.9 (GSANet)
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Video Object SegmentationonDAVIS 2016 val
J· 2020-01-19
80.5
best: 88.3 (GSANet)
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810
Video Object SegmentationonYouTube-Objects
J· 2020-01-19
70.5
best: 75.1 (FakeFlow)
See More, Know More: Unsupervised Video Object Segmentation with Co-Attention Siamese Networks arXiv:2001.06810