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 ⌀ |
|---|---|---|---|---|---|
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Full | FiFA | https://arxiv.org/abs/2402.15044v1 | Mean NME | 0.92 |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Full | FiFA | https://arxiv.org/abs/2402.15044v1 | NME | 0.92 |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Full | AnchorFace | https://arxiv.org/abs/2007.03221v3 | Mean NME | 1.56 |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Full | AnchorFace | https://arxiv.org/abs/2007.03221v3 | Mean NME | 1.56 |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Full | SAN | http://arxiv.org/abs/1803.04108v4 | Mean NME | 1.91 |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Full | DCFE (Box height Norm, 19 landmarks - no earlobs) | http://openaccess.thecvf.com/content_ECCV_2018/html/Roberto_Valle_A_Deeply-initialized_Coarse-to-fine_ECCV_2018_paper.html | Mean NME | 2.17 |
Facial Recognition and Modelling > Facial Landmark Detection | AFLW-Full | 3DDE (Box height Norm, 19 landmarks - no earlobs) | https://arxiv.org/abs/1902.01831v2 | Mean NME | 2.01 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | 300W | DVE | https://arxiv.org/abs/1908.06427v1 | NME | 4.65 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | 300W | FAb-Net | http://arxiv.org/abs/1808.06882v1 | NME | 5.71 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | 300W | FSE | http://arxiv.org/abs/1705.02193v2 | NME | 7.97 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | 300W | DEIL | http://arxiv.org/abs/1706.02932v2 | NME | 8.23 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | Deep Latent Particles | https://arxiv.org/abs/2205.15821v2 | NME | 2.43 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | Conditional Image Generation | http://arxiv.org/abs/1806.07823v2 | NME | 2.54 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | DVE | https://arxiv.org/abs/1908.06427v1 | NME | 2.86 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | LMDIS-REP | http://arxiv.org/abs/1804.04412v1 | NME | 3.15 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | Lorenz2019unsupervised | https://arxiv.org/abs/1903.06946v3 | NME | 3.24 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | FAB-Net | http://arxiv.org/abs/1808.06882v1 | NME | 3.44 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | AutoLink | https://arxiv.org/abs/2205.10636v6 | NME | 3.54 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | DEIL | http://arxiv.org/abs/1706.02932v2 | NME | 4.02 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | Deforming Autoencoders | http://arxiv.org/abs/1806.06503v1 | NME | 5.45 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | LatentKeypointGAN | https://arxiv.org/abs/2103.15812v5 | NME | 5.85 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | Thewlis2017unsupervised | http://arxiv.org/abs/1705.02193v2 | NME | 6.32 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | FSE | http://arxiv.org/abs/1705.02193v2 | NME | 6.67 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL | TCDCN | https://arxiv.org/abs/1408.3967v4 | NME | 7.95 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | AFLW-MTFL | DVE | https://arxiv.org/abs/1908.06427v1 | NME | 7.53 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | AFLW-MTFL | FSE | http://arxiv.org/abs/1705.02193v2 | NME | 10.53 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | AFLW-MTFL | DEIL | http://arxiv.org/abs/1706.02932v2 | NME | 10.99 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL Unaligned | AutoLink | https://arxiv.org/abs/2205.10636v6 | NME | 5.24 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL Unaligned | GANSeg | https://arxiv.org/abs/2112.01036v3 | NME | 6.18 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL Unaligned | IMM | http://arxiv.org/abs/1806.07823v2 | NME | 8.74 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL Unaligned | Lorenz2019unsupervised | https://arxiv.org/abs/1903.06946v3 | NME | 11.41 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL Unaligned | UPSDAS | https://arxiv.org/abs/2105.12405v1 | NME | 12.26 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL Unaligned | SCOPS | https://arxiv.org/abs/1905.01298v1 | NME | 15.01 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL Unaligned | DFF | http://arxiv.org/abs/1806.10206v5 | NME | 31.30 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL Unaligned | ULD | http://arxiv.org/abs/1705.02193v2 | NME | 31.3 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | MAFL Unaligned | LMDIS-REP | http://arxiv.org/abs/1804.04412v1 | NME | 40.82 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | AFLW Unaligned | UPSDAP | https://arxiv.org/abs/2105.12405v1 | NME | 13.13 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | AFLW Unaligned | IMM | https://arxiv.org/abs/2105.12405v1 | NME | 13.31 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | AFLW Unaligned | Lorenz2019unsupervised | https://arxiv.org/abs/2105.12405v1 | NME | 13.6 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | AFLW Unaligned | SCOPS | https://arxiv.org/abs/2105.12405v1 | NME | 16.05 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | AFLW (Zhang CVPR 2018 crops) | Conditional Image Generation | http://arxiv.org/abs/1806.07823v2 | NME | 6.31 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | AFLW (Zhang CVPR 2018 crops) | DVE | https://arxiv.org/abs/1908.06427v1 | NME | 6.54 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | AFLW (Zhang CVPR 2018 crops) | LMDIS-REP | http://arxiv.org/abs/1804.04412v1 | NME | 6.58 |
Facial Recognition and Modelling > Facial Landmark Detection > Unsupervised Facial Landmark Detection | AFLW (Zhang CVPR 2018 crops) | DEIL | http://arxiv.org/abs/1706.02932v2 | NME | 10.14 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | 3D-LFM | https://arxiv.org/abs/2312.11894v2 | Average MPJPE (mm) | 10.44 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | Large SimpleBaseline | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 14.6 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | SemGAN | https://arxiv.org/abs/2406.01196v1 | Average MPJPE (mm) | 15.95 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | Jointformer | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 17.8 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | CanonPose + 3D supervision | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 17.9 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | Large SimpleBaseline | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 19.8 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | Jointformer | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 19.8 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | CPN + Jointformer | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 20.7 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | CanonPose + 3D supervision | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 22.2 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | CanonPose | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 24.6 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | SimpleBaseline | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 24.6 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | Resnet50 | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 26.3 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | CanonPose | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 31.9 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | SHN + SimpleBaseline | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 32.5 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | H3WB | SimpleBaseline | https://arxiv.org/abs/2211.15692v2 | Average MPJPE (mm) | 34.0 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | AFLW2000-3D | JVCR | http://arxiv.org/abs/1801.09242v1 | GTE | 7.28 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | Urban Hyperspectral Image | Lucky Brand 13 | https://aclanthology.org/2020.coling-main.519 | 10°5 cm | 13.69 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | 3DFAW | JVCR | http://arxiv.org/abs/1801.09242v1 | CVGTCE | 3.46 |
Facial Recognition and Modelling > Facial Landmark Detection > 3D Facial Landmark Localization | 3DFAW | JVCR | http://arxiv.org/abs/1801.09242v1 | GTE | 4.35 |
Facial Recognition and Modelling > Face Identification | IJB-A | StyleFNM | https://arxiv.org/abs/2312.14544v1 | Accuracy | 94.90% |
Facial Recognition and Modelling > Face Identification | IJB-A | Deep Residual Equivariant Mapping | http://arxiv.org/abs/1803.00839v1 | Accuracy | 94.60% |
Facial Recognition and Modelling > Face Identification | IJB-A | FPN | http://arxiv.org/abs/1708.07517v2 | Accuracy | 91.4% |
Facial Recognition and Modelling > Face Identification | MegaFace | Cos+UNPG | https://arxiv.org/abs/2203.11593v2 | Accuracy | 99.27% |
Facial Recognition and Modelling > Face Identification | MegaFace | PartialFC + Glint360K + R100 | https://arxiv.org/abs/2010.05222v2 | Accuracy | 99.10% |
Facial Recognition and Modelling > Face Identification | MegaFace | Arc+UNPG | https://arxiv.org/abs/2203.11593v2 | Accuracy | 98.82% |
Facial Recognition and Modelling > Face Identification | MegaFace | Prodpoly | https://arxiv.org/abs/2006.13026v2 | Accuracy | 98.78% |
Facial Recognition and Modelling > Face Identification | MegaFace | GhostFaceNetV2-1 | https://ieeexplore.ieee.org/document/10098610 | Accuracy | 98.64% |
Facial Recognition and Modelling > Face Identification | MegaFace | ArcFace + MS1MV2 + R100 + R | https://arxiv.org/abs/1801.07698v4 | Accuracy | 98.35% |
Facial Recognition and Modelling > Face Identification | MegaFace | Mag+UNPG | https://arxiv.org/abs/2203.11593v2 | Accuracy | 98.03% |
Facial Recognition and Modelling > Face Identification | MegaFace | SV-AM-Softmax | http://arxiv.org/abs/1812.11317v1 | Accuracy | 97.2% |
Facial Recognition and Modelling > Face Identification | MegaFace | CosFace | http://arxiv.org/abs/1801.09414v2 | Accuracy | 82.72% |
Facial Recognition and Modelling > Face Identification | MegaFace | SphereFace (3-patch ensemble) | http://arxiv.org/abs/1704.08063v4 | Accuracy | 75.766% |
Facial Recognition and Modelling > Face Identification | MegaFace | Light CNN-29 | http://arxiv.org/abs/1511.02683v4 | Accuracy | 73.749% |
Facial Recognition and Modelling > Face Identification | MegaFace | SphereFace (single model) | http://arxiv.org/abs/1704.08063v4 | Accuracy | 72.729% |
Facial Recognition and Modelling > Face Identification | MegaFace | FaceNet | http://arxiv.org/abs/1503.03832v3 | Accuracy | 70.49% |
Facial Recognition and Modelling > Face Identification | Trillion Pairs Dataset | SV-AM-Softmax | http://arxiv.org/abs/1812.11317v1 | Accuracy | 73.56 |
Facial Recognition and Modelling > Face Identification | Trillion Pairs Dataset | AM-Softmax | http://arxiv.org/abs/1801.05599v4 | Accuracy | 61.80 |
Facial Recognition and Modelling > Face Identification | Trillion Pairs Dataset | Arc-Softmax | https://arxiv.org/abs/1801.07698v4 | Accuracy | 57.48 |
Facial Recognition and Modelling > Face Identification | Trillion Pairs Dataset | A-Softmax | http://arxiv.org/abs/1704.08063v4 | Accuracy | 43.89 |
Facial Recognition and Modelling > Face Identification | Trillion Pairs Dataset | F-Softmax | http://arxiv.org/abs/1708.02002v2 | Accuracy | 39.80 |
Facial Recognition and Modelling > Face Identification | Trillion Pairs Dataset | HM-Softmax | http://arxiv.org/abs/1604.03540v1 | Accuracy | 36.75 |
Facial Recognition and Modelling > Face Identification | DroneSURF | CoNAN (Adaface) | https://arxiv.org/abs/2307.10237v1 | Rank1 | 83.33 |
Facial Recognition and Modelling > Face Identification | DroneSURF | ProxyFusion (Adaface) | https://proceedings.neurips.cc/paper_files/paper/2024/hash/81f554467f27759e88de14ba2fbafb47-Abstract-Conference.html | Rank1 | 83.33 |
Facial Recognition and Modelling > Face Identification | DroneSURF | NAN (Adaface) | http://arxiv.org/abs/1603.05474v4 | Rank1 | 80.21 |
Facial Recognition and Modelling > Face Identification | DroneSURF | MCN (Adaface) | http://arxiv.org/abs/1807.09192v1 | Rank1 | 79.16 |
Facial Recognition and Modelling > Face Identification | DroneSURF | Naive Averaging (Adaface) | http://arxiv.org/abs/1312.4400v3 | Rank1 | 46.87 |
Facial Recognition and Modelling > Face Identification | DroneSURF | HOG | https://arxiv.org/abs/2204.01712v1 | Rank1 | 8.33 |
Facial Recognition and Modelling > Face Identification | IJB-B | FPN | http://arxiv.org/abs/1708.07517v2 | Accuracy | 91.1% |
Facial Recognition and Modelling > Facial Inpainting | WebFace | SymmFCNet (Full) | http://arxiv.org/abs/1812.07741v1 | PSNR | 27.22 |
Facial Recognition and Modelling > Facial Inpainting | FFHQ | DMFN | https://arxiv.org/abs/2002.02609v2 | LPIPS | 0.0457 |
Facial Recognition and Modelling > Facial Inpainting | FFHQ | DMFN | https://arxiv.org/abs/2002.02609v2 | PSNR | 26.49 |
Facial Recognition and Modelling > Facial Inpainting | FFHQ | DMFN | https://arxiv.org/abs/2002.02609v2 | SSIM | 0.8985 |
Facial Recognition and Modelling > Facial Inpainting | VggFace2 | SymmFCNet (Full) | http://arxiv.org/abs/1812.07741v1 | PSNR | 27.81 |
Facial Recognition and Modelling > Facial Action Unit Detection | DISFA | Norface | https://arxiv.org/abs/2407.15617v1 | Average F1 | 72.7 |
Facial Recognition and Modelling > Facial Action Unit Detection | DISFA | FMAE_IAT | https://arxiv.org/abs/2407.11243v2 | Average F1 | 70.1 |
Facial Recognition and Modelling > Facial Action Unit Detection | DISFA | FMAE | https://arxiv.org/abs/2407.11243v2 | Average F1 | 68.7 |
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