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Models/PLM

PLM

Reported on 43 benchmarks across 7 tasks · 2 papers · 2 SOTA

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

Computer Vision21 results

VideoonDAVIS 2016
F-measure (Decay)· 2017-08-17
14.7
best: 27.2 (OFL)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
VideoonDAVIS 2016
F-measure (Mean)· 2017-08-17
62.5
best: 94.7 (SwinB-DeAOT-L)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
VideoonDAVIS 2016
F-measure (Recall)· 2017-08-17
73.2
best: 97.1 (STCN)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
VideoonDAVIS 2016
J&F· 2017-08-17
66.35
best: 93.4 (ISVOS (BL30K, MS))
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
VideoonDAVIS 2016
Jaccard (Decay)· 2017-08-17
11.2
best: 28.9 (BVS)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
VideoonDAVIS 2016
Jaccard (Mean)· 2017-08-17
70.2
best: 92.5 (ISVOS (BL30K, MS))
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
VideoonDAVIS 2016
Jaccard (Recall)· 2017-08-17
86.3
best: 98.1 (STCN)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Video Object SegmentationonDAVIS 2016
F-measure (Decay)· 2017-08-17
14.7
best: 27.2 (OFL)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Video Object SegmentationonDAVIS 2016
F-measure (Mean)· 2017-08-17
62.5
best: 94.7 (SwinB-DeAOT-L)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Video Object SegmentationonDAVIS 2016
F-measure (Recall)· 2017-08-17
73.2
best: 97.1 (STCN)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Video Object SegmentationonDAVIS 2016
J&F· 2017-08-17
66.35
best: 93.4 (ISVOS (BL30K, MS))
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Video Object SegmentationonDAVIS 2016
Jaccard (Decay)· 2017-08-17
11.2
best: 28.9 (BVS)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Video Object SegmentationonDAVIS 2016
Jaccard (Mean)· 2017-08-17
70.2
best: 92.5 (ISVOS (BL30K, MS))
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Video Object SegmentationonDAVIS 2016
Jaccard (Recall)· 2017-08-17
86.3
best: 98.1 (STCN)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Semi-Supervised Video Object SegmentationonDAVIS 2016
F-measure (Decay)· 2017-08-17
14.7
best: 27.2 (OFL)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Semi-Supervised Video Object SegmentationonDAVIS 2016
F-measure (Mean)· 2017-08-17
62.5
best: 94.7 (SwinB-DeAOT-L)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Semi-Supervised Video Object SegmentationonDAVIS 2016
F-measure (Recall)· 2017-08-17
73.2
best: 97.1 (STCN)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Semi-Supervised Video Object SegmentationonDAVIS 2016
J&F· 2017-08-17
66.35
best: 93.4 (ISVOS (BL30K, MS))
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Semi-Supervised Video Object SegmentationonDAVIS 2016
Jaccard (Decay)· 2017-08-17
11.2
best: 28.9 (BVS)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Semi-Supervised Video Object SegmentationonDAVIS 2016
Jaccard (Mean)· 2017-08-17
70.2
best: 92.5 (ISVOS (BL30K, MS))
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137
Semi-Supervised Video Object SegmentationonDAVIS 2016
Jaccard (Recall)· 2017-08-17
86.3
best: 98.1 (STCN)
Pixel-Level Matching for Video Object Segmentation using Convolutional Neural Networks arXiv:1708.05137

Methodology11 results

Multi-Label ClassificationonMIMIC-IV-ICD-10-full
Micro-F1· 2023-04-27
56.95
SOTA
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification arXiv:2304.13998
Multi-Label ClassificationonMIMIC-IV-ICD-10-full
Macro-AUC· 2023-04-27
91.85
best: 97.07 (MSMN)
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification arXiv:2304.13998
Multi-Label ClassificationonMIMIC-IV-ICD-10-full
Macro-F1· 2023-04-27
4.9
best: 5.71 (Joint LAAT)
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification arXiv:2304.13998
Multi-Label ClassificationonMIMIC-IV-ICD-10-full
Micro-AUC· 2023-04-27
99.02
best: 99.61 (MSMN)
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification arXiv:2304.13998
Multi-Label ClassificationonMIMIC-IV-ICD-10-full
Precision@8· 2023-04-27
69.47
best: 6697 (LAAT)
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification arXiv:2304.13998
Domain AdaptationonVehicleID to VeRi-776
Rank-1
77.59
best: 80.4 (SPCL)
Domain AdaptationonVehicleID to VeRi-776
Rank-5
87
best: 89.05 (CORE-ReID V2)
Domain AdaptationonVehicleID to VeRi-776
mAP
47.37
best: 49.5 (CORE-ReID V2)
Domain AdaptationonVeri-776 to VehicleID Medium
R-1
45.4
best: 53.49 (CORE-ReID V2)
Domain AdaptationonVeri-776 to VehicleID Medium
R-5
63.37
best: 74.36 (CORE-ReID V2)
Domain AdaptationonVeri-776 to VehicleID Medium
mAP
49.41
best: 63.02 (CORE-ReID V2)

Other6 results

Unsupervised Domain AdaptationonVehicleID to VeRi-776
Rank-1
77.59
best: 80.4 (SPCL)
Unsupervised Domain AdaptationonVehicleID to VeRi-776
Rank-5
87
best: 89.05 (CORE-ReID V2)
Unsupervised Domain AdaptationonVehicleID to VeRi-776
mAP
47.37
best: 49.5 (CORE-ReID V2)
Unsupervised Domain AdaptationonVeri-776 to VehicleID Medium
R-1
45.4
best: 53.49 (CORE-ReID V2)
Unsupervised Domain AdaptationonVeri-776 to VehicleID Medium
R-5
63.37
best: 74.36 (CORE-ReID V2)
Unsupervised Domain AdaptationonVeri-776 to VehicleID Medium
mAP
49.41
best: 63.02 (CORE-ReID V2)

Medical5 results

Medical Code PredictiononMIMIC-IV-ICD-10-full
Micro-F1· 2023-04-27
56.95
SOTA
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification arXiv:2304.13998
Medical Code PredictiononMIMIC-IV-ICD-10-full
Macro-AUC· 2023-04-27
91.85
best: 97.07 (MSMN)
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification arXiv:2304.13998
Medical Code PredictiononMIMIC-IV-ICD-10-full
Macro-F1· 2023-04-27
4.9
best: 5.71 (Joint LAAT)
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification arXiv:2304.13998
Medical Code PredictiononMIMIC-IV-ICD-10-full
Micro-AUC· 2023-04-27
99.02
best: 99.61 (MSMN)
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification arXiv:2304.13998
Medical Code PredictiononMIMIC-IV-ICD-10-full
Precision@8· 2023-04-27
69.47
best: 6697 (LAAT)
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification arXiv:2304.13998