task_path stringlengths 3 199 ⌀ | dataset stringlengths 1 128 ⌀ | model_name stringlengths 1 223 ⌀ | paper_url stringlengths 21 601 ⌀ | metric_name stringlengths 1 50 ⌀ | metric_value stringlengths 1 9.22k ⌀ |
|---|---|---|---|---|---|
Depth Estimation > Depth And Camera Motion > Face Anti-Spoofing | Replay-Attack | 3D Synthesis (balancing sampling) | https://arxiv.org/abs/1901.00488v3 | HTER | 0.63 |
Depth Estimation > Depth And Camera Motion > Face Anti-Spoofing | Replay-Attack | YCbCr+HSV-LBP | http://arxiv.org/abs/1511.06316v1 | EER | 0.40 |
Depth Estimation > Depth And Camera Motion > Face Anti-Spoofing | Replay-Attack | YCbCr+HSV-LBP | http://arxiv.org/abs/1511.06316v1 | HTER | 2.90 |
Depth Estimation > Depth And Camera Motion > Face Anti-Spoofing | Replay-Attack | Multi-Scale | https://arxiv.org/abs/1408.5601v2 | EER | 2.14 |
Depth Estimation > Depth And Camera Motion > Face Anti-Spoofing | MSU-MFSD | Entry-V2 | https://arxiv.org/abs/2206.06510v1 | Equal Error Rate | 0 |
Depth Estimation > Depth And Camera Motion > Face Anti-Spoofing | MSU-MFSD | Entry-V2 | https://arxiv.org/abs/2206.06510v1 | HTER | 0 |
Depth Estimation > Depth And Camera Motion > Face Anti-Spoofing | MSU-MFSD | GFA-CNN | http://arxiv.org/abs/1901.05602v1 | Equal Error Rate | 7.5% |
Depth Estimation > Depth And Camera Motion > Face Anti-Spoofing | MSU-MFSD | Color LBP | http://arxiv.org/abs/1511.06316v1 | Equal Error Rate | 10.8% |
Depth Estimation > 3D Depth Estimation | Relative Human | BEV | https://arxiv.org/abs/2112.08274v3 | PCDR | 68.27 |
Depth Estimation > 3D Depth Estimation | Relative Human | BEV | https://arxiv.org/abs/2112.08274v3 | PCDR-Baby | 60.77 |
Depth Estimation > 3D Depth Estimation | Relative Human | BEV | https://arxiv.org/abs/2112.08274v3 | PCDR-Kid | 67.09 |
Depth Estimation > 3D Depth Estimation | Relative Human | BEV | https://arxiv.org/abs/2112.08274v3 | PCDR-Teen | 66.07 |
Depth Estimation > 3D Depth Estimation | Relative Human | BEV | https://arxiv.org/abs/2112.08274v3 | PCDR-Adult | 69.71 |
Depth Estimation > 3D Depth Estimation | Relative Human | BEV | https://arxiv.org/abs/2112.08274v3 | mPCDK | 0.884 |
Depth Estimation > 3D Depth Estimation | Relative Human | ROMP | https://arxiv.org/abs/2008.12272v4 | PCDR | 54.84 |
Depth Estimation > 3D Depth Estimation | Relative Human | ROMP | https://arxiv.org/abs/2008.12272v4 | PCDR-Baby | 30.08 |
Depth Estimation > 3D Depth Estimation | Relative Human | ROMP | https://arxiv.org/abs/2008.12272v4 | PCDR-Kid | 48.41 |
Depth Estimation > 3D Depth Estimation | Relative Human | ROMP | https://arxiv.org/abs/2008.12272v4 | PCDR-Teen | 51.12 |
Depth Estimation > 3D Depth Estimation | Relative Human | ROMP | https://arxiv.org/abs/2008.12272v4 | PCDR-Adult | 55.34 |
Depth Estimation > 3D Depth Estimation | Relative Human | ROMP | https://arxiv.org/abs/2008.12272v4 | mPCDK | 0.866 |
Depth Estimation > 3D Depth Estimation | Relative Human | CRMH | https://arxiv.org/abs/2006.08586v1 | PCDR | 54.83 |
Depth Estimation > 3D Depth Estimation | Relative Human | CRMH | https://arxiv.org/abs/2006.08586v1 | PCDR-Baby | 34.74 |
Depth Estimation > 3D Depth Estimation | Relative Human | CRMH | https://arxiv.org/abs/2006.08586v1 | PCDR-Kid | 48.37 |
Depth Estimation > 3D Depth Estimation | Relative Human | CRMH | https://arxiv.org/abs/2006.08586v1 | PCDR-Teen | 59.11 |
Depth Estimation > 3D Depth Estimation | Relative Human | CRMH | https://arxiv.org/abs/2006.08586v1 | PCDR-Adult | 55.47 |
Depth Estimation > 3D Depth Estimation | Relative Human | CRMH | https://arxiv.org/abs/2006.08586v1 | mPCDK | 0.781 |
Depth Estimation > 3D Depth Estimation > Transparent Object Depth Estimation | TransCG | DFNet | https://arxiv.org/abs/2202.08471v2 | RMSE | 0.018 |
Depth Estimation > 3D Depth Estimation > Transparent Object Depth Estimation | TransCG | DFNet | https://arxiv.org/abs/2202.08471v2 | REL | 0.027 |
Depth Estimation > 3D Depth Estimation > Transparent Object Depth Estimation | TransCG | DFNet | https://arxiv.org/abs/2202.08471v2 | MAE | 0.012 |
Depth Estimation > 3D Depth Estimation > Transparent Object Depth Estimation | TransCG | DFNet | https://arxiv.org/abs/2202.08471v2 | delta < 1.05 | 83.76 |
Depth Estimation > 3D Depth Estimation > Transparent Object Depth Estimation | TransCG | DFNet | https://arxiv.org/abs/2202.08471v2 | delta < 1.10 | 95.67 |
Depth Estimation > 3D Depth Estimation > Transparent Object Depth Estimation | TransCG | DFNet | https://arxiv.org/abs/2202.08471v2 | Delta < 1.25 | 99.71 |
Depth Estimation > Stereo-LiDAR Fusion | KITTI Depth Completion Validation | Volumetric Propagation Network | https://arxiv.org/abs/2103.12964v1 | RMSE | 636.2 |
Depth Estimation > Stereo-LiDAR Fusion | KITTI Depth Completion Validation | CSPN++ | https://arxiv.org/abs/1911.05377v2 | RMSE | 725.43 |
Depth Estimation > Stereo-LiDAR Fusion | KITTI Depth Completion Validation | CCVN | http://arxiv.org/abs/1904.02917v1 | RMSE | 749.3 |
Depth Estimation > Stereo-LiDAR Fusion | KITTI Depth Completion Validation | LiStereo | https://arxiv.org/abs/1905.02744v3 | RMSE | 749.3 |
Depth Estimation > Stereo-LiDAR Fusion | KITTI Depth Completion Validation | NLSPN | https://arxiv.org/abs/2007.10042v1 | RMSE | 771.8 |
Depth Estimation > Stereo-LiDAR Fusion | KITTI Depth Completion Validation | GuideNet | https://arxiv.org/abs/1908.01238v1 | RMSE | 777.78 |
Depth Estimation > Stereo-LiDAR Fusion | KITTI Depth Completion Validation | PSMNet | http://arxiv.org/abs/1803.08669v1 | RMSE | 884 |
Depth Estimation > Stereo-LiDAR Fusion | KITTI Depth Completion Validation | SCADC | https://arxiv.org/abs/2003.06945v4 | RMSE | 1009.6 |
Depth Estimation > Stereo-LiDAR Fusion | KITTI Depth Completion Validation | GCNet | http://arxiv.org/abs/1703.04309v1 | RMSE | 1031.4 |
Depth Estimation > Indoor Monocular Depth Estimation | DIODE | LeReS | https://arxiv.org/abs/2012.09365v1 | Delta < 1.25^3 | 0.900 |
Depth Estimation > Indoor Monocular Depth Estimation | DIODE | AIP-Brown | https://arxiv.org/abs/2007.06153v1 | Delta < 1.25^3 | 0.7945 |
Depth Estimation > Depth Aleatoric Uncertainty Estimation | Mid-Air Dataset | M4Depth+U | https://arxiv.org/abs/2305.19780v1 | AuSE on Abs Rel | 0.007 |
Depth Estimation > Depth Aleatoric Uncertainty Estimation | Mid-Air Dataset | M4Depth+U | https://arxiv.org/abs/2305.19780v1 | AuSE on RMSE log | 0.02 |
Diabetes Prediction | Diabetes | XBNET | https://arxiv.org/abs/2106.05239v3 | Accuracy | 78.78 |
Medical Code Prediction | MIMIC-III | GKI-ICD | https://arxiv.org/abs/2505.18708v1 | Macro-AUC | 96.2 |
Medical Code Prediction | MIMIC-III | GKI-ICD | https://arxiv.org/abs/2505.18708v1 | Micro-AUC | 99.3 |
Medical Code Prediction | MIMIC-III | GKI-ICD | https://arxiv.org/abs/2505.18708v1 | Macro-F1 | 12.3 |
Medical Code Prediction | MIMIC-III | GKI-ICD | https://arxiv.org/abs/2505.18708v1 | Micro-F1 | 61.2 |
Medical Code Prediction | MIMIC-III | GKI-ICD | https://arxiv.org/abs/2505.18708v1 | Precision@8 | 77.7 |
Medical Code Prediction | MIMIC-III | GKI-ICD | https://arxiv.org/abs/2505.18708v1 | Precision@15 | 62.4 |
Medical Code Prediction | MIMIC-III | GKI-ICD | https://arxiv.org/abs/2505.18708v1 | mAP | 66.1 |
Medical Code Prediction | MIMIC-III | PLM-CA | https://arxiv.org/abs/2406.08958v2 | Macro-F1 | 24.7 |
Medical Code Prediction | MIMIC-III | PLM-CA | https://arxiv.org/abs/2406.08958v2 | Micro-F1 | 60.0 |
Medical Code Prediction | MIMIC-III | PLM-CA | https://arxiv.org/abs/2406.08958v2 | mAP | 64.7 |
Medical Code Prediction | MIMIC-III | MSMN+KEPTLongformer | https://arxiv.org/abs/2210.03304v2 | Macro-F1 | 11.8 |
Medical Code Prediction | MIMIC-III | MSMN+KEPTLongformer | https://arxiv.org/abs/2210.03304v2 | Micro-F1 | 59.9 |
Medical Code Prediction | MIMIC-III | MSMN+KEPTLongformer | https://arxiv.org/abs/2210.03304v2 | Precision@8 | 77.1 |
Medical Code Prediction | MIMIC-III | MSMN+KEPTLongformer | https://arxiv.org/abs/2210.03304v2 | Precision@15 | 61.5 |
Medical Code Prediction | MIMIC-III | EffectiveCAN | https://aclanthology.org/2021.emnlp-main.481 | Macro-AUC | 91.5 |
Medical Code Prediction | MIMIC-III | EffectiveCAN | https://aclanthology.org/2021.emnlp-main.481 | Micro-AUC | 98.8 |
Medical Code Prediction | MIMIC-III | EffectiveCAN | https://aclanthology.org/2021.emnlp-main.481 | Macro-F1 | 10.6 |
Medical Code Prediction | MIMIC-III | EffectiveCAN | https://aclanthology.org/2021.emnlp-main.481 | Micro-F1 | 58.9 |
Medical Code Prediction | MIMIC-III | EffectiveCAN | https://aclanthology.org/2021.emnlp-main.481 | Precision@8 | 75.8 |
Medical Code Prediction | MIMIC-III | EffectiveCAN | https://aclanthology.org/2021.emnlp-main.481 | Precision@15 | 60.6 |
Medical Code Prediction | MIMIC-III | Discnet+RE | https://aclanthology.org/2022.coling-1.254 | Macro-AUC | 95.6 |
Medical Code Prediction | MIMIC-III | Discnet+RE | https://aclanthology.org/2022.coling-1.254 | Micro-AUC | 99.3 |
Medical Code Prediction | MIMIC-III | Discnet+RE | https://aclanthology.org/2022.coling-1.254 | Macro-F1 | 14.0 |
Medical Code Prediction | MIMIC-III | Discnet+RE | https://aclanthology.org/2022.coling-1.254 | Micro-F1 | 58.8 |
Medical Code Prediction | MIMIC-III | Discnet+RE | https://aclanthology.org/2022.coling-1.254 | Precision@8 | 76.5 |
Medical Code Prediction | MIMIC-III | Discnet+RE | https://aclanthology.org/2022.coling-1.254 | Precision@15 | 61.4 |
Medical Code Prediction | MIMIC-III | RAC | https://arxiv.org/abs/2107.10650v1 | Macro-AUC | 94.8 |
Medical Code Prediction | MIMIC-III | RAC | https://arxiv.org/abs/2107.10650v1 | Micro-AUC | 99.2 |
Medical Code Prediction | MIMIC-III | RAC | https://arxiv.org/abs/2107.10650v1 | Macro-F1 | 12.7 |
Medical Code Prediction | MIMIC-III | RAC | https://arxiv.org/abs/2107.10650v1 | Micro-F1 | 58.6 |
Medical Code Prediction | MIMIC-III | RAC | https://arxiv.org/abs/2107.10650v1 | Precision@5 | 82.9 |
Medical Code Prediction | MIMIC-III | RAC | https://arxiv.org/abs/2107.10650v1 | Precision@8 | 75.4 |
Medical Code Prediction | MIMIC-III | RAC | https://arxiv.org/abs/2107.10650v1 | Precision@15 | 60.1 |
Medical Code Prediction | MIMIC-III | MSMN | https://arxiv.org/abs/2203.01515v2 | Macro-AUC | 95.0 |
Medical Code Prediction | MIMIC-III | MSMN | https://arxiv.org/abs/2203.01515v2 | Micro-AUC | 99.2 |
Medical Code Prediction | MIMIC-III | MSMN | https://arxiv.org/abs/2203.01515v2 | Macro-F1 | 10.3 |
Medical Code Prediction | MIMIC-III | MSMN | https://arxiv.org/abs/2203.01515v2 | Micro-F1 | 58.4 |
Medical Code Prediction | MIMIC-III | MSMN | https://arxiv.org/abs/2203.01515v2 | Precision@8 | 75.2 |
Medical Code Prediction | MIMIC-III | MSMN | https://arxiv.org/abs/2203.01515v2 | Precision@15 | 59.9 |
Medical Code Prediction | MIMIC-III | JointLAAT | https://arxiv.org/abs/2007.06351v1 | Macro-AUC | 92.1 |
Medical Code Prediction | MIMIC-III | JointLAAT | https://arxiv.org/abs/2007.06351v1 | Micro-AUC | 98.8 |
Medical Code Prediction | MIMIC-III | JointLAAT | https://arxiv.org/abs/2007.06351v1 | Macro-F1 | 10.7 |
Medical Code Prediction | MIMIC-III | JointLAAT | https://arxiv.org/abs/2007.06351v1 | Micro-F1 | 57.5 |
Medical Code Prediction | MIMIC-III | JointLAAT | https://arxiv.org/abs/2007.06351v1 | Precision@5 | 80.6 |
Medical Code Prediction | MIMIC-III | JointLAAT | https://arxiv.org/abs/2007.06351v1 | Precision@8 | 73.5 |
Medical Code Prediction | MIMIC-III | JointLAAT | https://arxiv.org/abs/2007.06351v1 | Precision@15 | 59.0 |
Medical Code Prediction | MIMIC-III | LAAT | https://arxiv.org/abs/2007.06351v1 | Macro-AUC | 91.9 |
Medical Code Prediction | MIMIC-III | LAAT | https://arxiv.org/abs/2007.06351v1 | Micro-AUC | 98.8 |
Medical Code Prediction | MIMIC-III | LAAT | https://arxiv.org/abs/2007.06351v1 | Macro-F1 | 9.9 |
Medical Code Prediction | MIMIC-III | LAAT | https://arxiv.org/abs/2007.06351v1 | Micro-F1 | 57.5 |
Medical Code Prediction | MIMIC-III | LAAT | https://arxiv.org/abs/2007.06351v1 | Precision@5 | 81.3 |
Medical Code Prediction | MIMIC-III | LAAT | https://arxiv.org/abs/2007.06351v1 | Precision@8 | 73.8 |
Medical Code Prediction | MIMIC-III | LAAT | https://arxiv.org/abs/2007.06351v1 | Precision@15 | 59.1 |
Medical Code Prediction | MIMIC-III | MSATT-KG | null | Macro-AUC | 91.0 |
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