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Models/RCRNet+NER

RCRNet+NER

Reported on 72 benchmarks across 8 tasks · 1 paper · 72 SOTA

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

Computer Vision36 results

VideoonFBMS-59
AVERAGE MAE· uses extra data· 2019-08-12
0.054
best: 0.028 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
VideoonFBMS-59
MAX F-MEASURE· uses extra data· 2019-08-12
0.861
best: 0.906 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
VideoonFBMS-59
S-Measure· uses extra data· 2019-08-12
0.87
best: 0.926 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
VideoonVOS-T
Average MAE· uses extra data· 2019-08-12
0.049
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
VideoonVOS-T
S-Measure· uses extra data· 2019-08-12
0.872
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
VideoonVOS-T
max E-measure· uses extra data· 2019-08-12
0.856
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
VideoonDAVIS-2016
AVERAGE MAE· uses extra data· 2019-08-12
0.028
best: 0.01 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
VideoonDAVIS-2016
MAX F-MEASURE· uses extra data· 2019-08-12
0.859
best: 0.939 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
VideoonDAVIS-2016
S-Measure· uses extra data· 2019-08-12
0.884
best: 0.945 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Object DetectiononFBMS-59
AVERAGE MAE· uses extra data· 2019-08-12
0.054
best: 0.028 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Object DetectiononFBMS-59
MAX F-MEASURE· uses extra data· 2019-08-12
0.861
best: 0.906 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Object DetectiononFBMS-59
S-Measure· uses extra data· 2019-08-12
0.87
best: 0.926 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Object DetectiononVOS-T
Average MAE· uses extra data· 2019-08-12
0.049
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Object DetectiononVOS-T
S-Measure· uses extra data· 2019-08-12
0.872
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Object DetectiononVOS-T
max E-measure· uses extra data· 2019-08-12
0.856
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Object DetectiononDAVIS-2016
AVERAGE MAE· uses extra data· 2019-08-12
0.028
best: 0.01 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Object DetectiononDAVIS-2016
MAX F-MEASURE· uses extra data· 2019-08-12
0.859
best: 0.939 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Object DetectiononDAVIS-2016
S-Measure· uses extra data· 2019-08-12
0.884
best: 0.945 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Video Object SegmentationonFBMS-59
AVERAGE MAE· uses extra data· 2019-08-12
0.054
best: 0.028 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Video Object SegmentationonFBMS-59
MAX F-MEASURE· uses extra data· 2019-08-12
0.861
best: 0.906 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Video Object SegmentationonFBMS-59
S-Measure· uses extra data· 2019-08-12
0.87
best: 0.926 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Video Object SegmentationonVOS-T
Average MAE· uses extra data· 2019-08-12
0.049
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Video Object SegmentationonVOS-T
S-Measure· uses extra data· 2019-08-12
0.872
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Video Object SegmentationonVOS-T
max E-measure· uses extra data· 2019-08-12
0.856
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Video Object SegmentationonDAVIS-2016
AVERAGE MAE· uses extra data· 2019-08-12
0.028
best: 0.01 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Video Object SegmentationonDAVIS-2016
MAX F-MEASURE· uses extra data· 2019-08-12
0.859
best: 0.939 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
Video Object SegmentationonDAVIS-2016
S-Measure· uses extra data· 2019-08-12
0.884
best: 0.945 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
RGB Salient Object DetectiononFBMS-59
AVERAGE MAE· uses extra data· 2019-08-12
0.054
best: 0.028 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
RGB Salient Object DetectiononFBMS-59
MAX F-MEASURE· uses extra data· 2019-08-12
0.861
best: 0.906 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
RGB Salient Object DetectiononFBMS-59
S-Measure· uses extra data· 2019-08-12
0.87
best: 0.926 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
RGB Salient Object DetectiononVOS-T
Average MAE· uses extra data· 2019-08-12
0.049
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
RGB Salient Object DetectiononVOS-T
S-Measure· uses extra data· 2019-08-12
0.872
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
RGB Salient Object DetectiononVOS-T
max E-measure· uses extra data· 2019-08-12
0.856
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
RGB Salient Object DetectiononDAVIS-2016
AVERAGE MAE· uses extra data· 2019-08-12
0.028
best: 0.01 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
RGB Salient Object DetectiononDAVIS-2016
MAX F-MEASURE· uses extra data· 2019-08-12
0.859
best: 0.939 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
RGB Salient Object DetectiononDAVIS-2016
S-Measure· uses extra data· 2019-08-12
0.884
best: 0.945 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051

Methodology36 results

3DonFBMS-59
AVERAGE MAE· uses extra data· 2019-08-12
0.054
best: 0.028 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
3DonFBMS-59
MAX F-MEASURE· uses extra data· 2019-08-12
0.861
best: 0.906 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
3DonFBMS-59
S-Measure· uses extra data· 2019-08-12
0.87
best: 0.926 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
3DonVOS-T
Average MAE· uses extra data· 2019-08-12
0.049
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
3DonVOS-T
S-Measure· uses extra data· 2019-08-12
0.872
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
3DonVOS-T
max E-measure· uses extra data· 2019-08-12
0.856
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
3DonDAVIS-2016
AVERAGE MAE· uses extra data· 2019-08-12
0.028
best: 0.01 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
3DonDAVIS-2016
MAX F-MEASURE· uses extra data· 2019-08-12
0.859
best: 0.939 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
3DonDAVIS-2016
S-Measure· uses extra data· 2019-08-12
0.884
best: 0.945 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D ClassificationonFBMS-59
AVERAGE MAE· uses extra data· 2019-08-12
0.054
best: 0.028 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D ClassificationonFBMS-59
MAX F-MEASURE· uses extra data· 2019-08-12
0.861
best: 0.906 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D ClassificationonFBMS-59
S-Measure· uses extra data· 2019-08-12
0.87
best: 0.926 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D ClassificationonVOS-T
Average MAE· uses extra data· 2019-08-12
0.049
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D ClassificationonVOS-T
S-Measure· uses extra data· 2019-08-12
0.872
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D ClassificationonVOS-T
max E-measure· uses extra data· 2019-08-12
0.856
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D ClassificationonDAVIS-2016
AVERAGE MAE· uses extra data· 2019-08-12
0.028
best: 0.01 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D ClassificationonDAVIS-2016
MAX F-MEASURE· uses extra data· 2019-08-12
0.859
best: 0.939 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D ClassificationonDAVIS-2016
S-Measure· uses extra data· 2019-08-12
0.884
best: 0.945 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D Object DetectiononFBMS-59
AVERAGE MAE· uses extra data· 2019-08-12
0.054
best: 0.028 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D Object DetectiononFBMS-59
MAX F-MEASURE· uses extra data· 2019-08-12
0.861
best: 0.906 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D Object DetectiononFBMS-59
S-Measure· uses extra data· 2019-08-12
0.87
best: 0.926 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D Object DetectiononVOS-T
Average MAE· uses extra data· 2019-08-12
0.049
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D Object DetectiononVOS-T
S-Measure· uses extra data· 2019-08-12
0.872
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D Object DetectiononVOS-T
max E-measure· uses extra data· 2019-08-12
0.856
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D Object DetectiononDAVIS-2016
AVERAGE MAE· uses extra data· 2019-08-12
0.028
best: 0.01 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D Object DetectiononDAVIS-2016
MAX F-MEASURE· uses extra data· 2019-08-12
0.859
best: 0.939 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
2D Object DetectiononDAVIS-2016
S-Measure· uses extra data· 2019-08-12
0.884
best: 0.945 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
16konFBMS-59
AVERAGE MAE· uses extra data· 2019-08-12
0.054
best: 0.028 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
16konFBMS-59
MAX F-MEASURE· uses extra data· 2019-08-12
0.861
best: 0.906 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
16konFBMS-59
S-Measure· uses extra data· 2019-08-12
0.87
best: 0.926 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
16konVOS-T
Average MAE· uses extra data· 2019-08-12
0.049
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
16konVOS-T
S-Measure· uses extra data· 2019-08-12
0.872
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
16konVOS-T
max E-measure· uses extra data· 2019-08-12
0.856
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
16konDAVIS-2016
AVERAGE MAE· uses extra data· 2019-08-12
0.028
best: 0.01 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
16konDAVIS-2016
MAX F-MEASURE· uses extra data· 2019-08-12
0.859
best: 0.939 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051
16konDAVIS-2016
S-Measure· uses extra data· 2019-08-12
0.884
best: 0.945 (RealFlow)
SOTA
Semi-Supervised Video Salient Object Detection Using Pseudo-Labels arXiv:1908.04051