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 > Face Verification | MegaFace | SV-AM-Softmax | http://arxiv.org/abs/1812.11317v1 | Accuracy | 97.38% |
Facial Recognition and Modelling > Face Verification | MegaFace | CosFace | http://arxiv.org/abs/1801.09414v2 | Accuracy | 96.65% |
Facial Recognition and Modelling > Face Verification | MegaFace | PFEfuse + match | https://arxiv.org/abs/1904.09658v4 | Accuracy | 92.51% |
Facial Recognition and Modelling > Face Verification | MegaFace | SphereFace (3-patch ensemble) | http://arxiv.org/abs/1704.08063v4 | Accuracy | 89.142% |
Facial Recognition and Modelling > Face Verification | MegaFace | SphereFace (single model) | http://arxiv.org/abs/1704.08063v4 | Accuracy | 85.561% |
Facial Recognition and Modelling > Face Verification | MegaFace | Light CNN-29 | http://arxiv.org/abs/1511.02683v4 | Accuracy | 85.133% |
Facial Recognition and Modelling > Face Verification | CK+ | SphereFace | http://arxiv.org/abs/1704.08063v4 | Accuracy | 93.80 |
Facial Recognition and Modelling > Face Verification | Trillion Pairs Dataset | SV-AM-Softmax | http://arxiv.org/abs/1812.11317v1 | Accuracy | 72.71 |
Facial Recognition and Modelling > Face Verification | Trillion Pairs Dataset | AM-Softmax | http://arxiv.org/abs/1801.05599v4 | Accuracy | 61.61 |
Facial Recognition and Modelling > Face Verification | Trillion Pairs Dataset | Arc-Softmax | https://arxiv.org/abs/1801.07698v4 | Accuracy | 57.45 |
Facial Recognition and Modelling > Face Verification | Trillion Pairs Dataset | A-Softmax | http://arxiv.org/abs/1704.08063v4 | Accuracy | 43.76 |
Facial Recognition and Modelling > Face Verification | Trillion Pairs Dataset | F-Softmax | http://arxiv.org/abs/1708.02002v2 | Accuracy | 37.14 |
Facial Recognition and Modelling > Face Verification | Trillion Pairs Dataset | HM-Softmax | http://arxiv.org/abs/1604.03540v1 | Accuracy | 34.46 |
Facial Recognition and Modelling > Face Verification | BUAA-VisNir | LightCNN-29 + DVG | https://arxiv.org/abs/1903.10203v3 | TAR @ FAR=0.001 | 97.3 |
Facial Recognition and Modelling > Face Verification | BUAA-VisNir | LightCNN-29 + DVG | https://arxiv.org/abs/1903.10203v3 | TAR @ FAR=0.01 | 98.5 |
Facial Recognition and Modelling > Face Verification | BUAA-VisNir | DVR Wu et al. (2019) | http://arxiv.org/abs/1809.01936v3 | TAR @ FAR=0.001 | 96.9 |
Facial Recognition and Modelling > Face Verification | BUAA-VisNir | DVR Wu et al. (2019) | http://arxiv.org/abs/1809.01936v3 | TAR @ FAR=0.01 | 98.5 |
Facial Recognition and Modelling > Face Verification | BUAA-VisNir | W-CNN He et al. (2018) | http://arxiv.org/abs/1708.02412v1 | TAR @ FAR=0.001 | 91.9 |
Facial Recognition and Modelling > Face Verification | BUAA-VisNir | W-CNN He et al. (2018) | http://arxiv.org/abs/1708.02412v1 | TAR @ FAR=0.01 | 96.0 |
Facial Recognition and Modelling > Face Verification | Labeled Faces in the Wild | ArcFace + MS1MV2 + R100, | https://arxiv.org/abs/1801.07698v4 | Accuracy | 99.83% |
Facial Recognition and Modelling > Face Verification | Labeled Faces in the Wild | FaceNet | http://arxiv.org/abs/1503.03832v3 | Accuracy | 99.63% |
Facial Recognition and Modelling > Face Verification | Labeled Faces in the Wild | Dlib | https://www.jmlr.org/papers/volume10/king09a/king09a.pdf | Accuracy | 99.38% |
Facial Recognition and Modelling > Face Verification | Labeled Faces in the Wild | VGG-Face | https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/ | Accuracy | 98.78% |
Facial Recognition and Modelling > Face Verification | Labeled Faces in the Wild | DeepFace | https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification/ | Accuracy | 98.37% |
Facial Recognition and Modelling > Face Verification | Labeled Faces in the Wild | DeepID | http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf | Accuracy | 97.05% |
Facial Recognition and Modelling > Face Verification | Labeled Faces in the Wild | OpenFace | http://reports-archive.adm.cs.cmu.edu/anon/anon/2016/CMU-CS-16-118.pdf | Accuracy | 92.92% |
Facial Recognition and Modelling > Face Verification | Oulu-CASIA | DeepId2+ | https://arxiv.org/abs/1412.1265v1 | Accuracy | 96.50 |
Facial Recognition and Modelling > Face Verification | IIIT-D Viewed Sketch | LightCNN-29 + DVG | https://arxiv.org/abs/1903.10203v3 | TAR @ FAR=0.01 | 97.86 |
Facial Recognition and Modelling > Face Verification | IJB-S | AdaFace+CSFM | https://arxiv.org/abs/2207.10180v1 | Rank-1 (Video2Single) | 72.54 |
Facial Recognition and Modelling > Face Verification | IJB-S | AdaFace+CSFM | https://arxiv.org/abs/2207.10180v1 | Rank-1 (Video2Booking) | 72.65 |
Facial Recognition and Modelling > Face Verification | IJB-S | AdaFace+CSFM | https://arxiv.org/abs/2207.10180v1 | Rank-1 (Video2Video) | 39.14 |
Facial Recognition and Modelling > Face Verification | IJB-S | ArcFace+CSFM | https://arxiv.org/abs/2207.10180v1 | Rank-1 (Video2Single) | 63.86 |
Facial Recognition and Modelling > Face Verification | IJB-S | ArcFace+CSFM | https://arxiv.org/abs/2207.10180v1 | Rank-1 (Video2Booking) | 65.95 |
Facial Recognition and Modelling > Face Verification | IJB-S | ArcFace+CSFM | https://arxiv.org/abs/2207.10180v1 | Rank-1 (Video2Video) | 21.38 |
Facial Recognition and Modelling > Face Verification | QMUL-SurvFace | DiscFace | https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html | TAR @ FAR=0.1 | 35.9 |
Facial Recognition and Modelling > Face Verification | CASIA NIR-VIS 2.0 | LightCNN-29 + DVG | https://arxiv.org/abs/1903.10203v3 | TAR @ FAR=0.001 | 99.8 |
Facial Recognition and Modelling > Face Verification | CASIA NIR-VIS 2.0 | DVR Wu et al. (2019) | http://arxiv.org/abs/1809.01936v3 | TAR @ FAR=0.001 | 99.6 |
Facial Recognition and Modelling > Face Verification | CASIA NIR-VIS 2.0 | W-CNN He et al. (2018) | http://arxiv.org/abs/1708.02412v1 | TAR @ FAR=0.001 | 98.4 |
Facial Recognition and Modelling > Face Verification | IJB-B | QMagFace | https://arxiv.org/abs/2111.13475v3 | TAR @ FAR=0.01 | 97.72% |
Facial Recognition and Modelling > Face Verification | IJB-B | QMagFace | https://arxiv.org/abs/2111.13475v3 | TAR @ FAR=0.001 | 96.48 |
Facial Recognition and Modelling > Face Verification | IJB-B | QMagFace | https://arxiv.org/abs/2111.13475v3 | TAR@FAR=0.0001 | 94.7 |
Facial Recognition and Modelling > Face Verification | IJB-B | QMagFace | https://arxiv.org/abs/2111.13475v3 | TAR @ FAR=0.0001 | 94.7 |
Facial Recognition and Modelling > Face Verification | IJB-B | Arc+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR @ FAR=0.01 | 97.7% |
Facial Recognition and Modelling > Face Verification | IJB-B | Arc+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR @ FAR=0.001 | 96.6 |
Facial Recognition and Modelling > Face Verification | IJB-B | Arc+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR@FAR=0.0001 | 95.04 |
Facial Recognition and Modelling > Face Verification | IJB-B | Mag+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR @ FAR=0.01 | 97.63% |
Facial Recognition and Modelling > Face Verification | IJB-B | Mag+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR @ FAR=0.001 | 96.5 |
Facial Recognition and Modelling > Face Verification | IJB-B | Mag+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR@FAR=0.0001 | 95.21 |
Facial Recognition and Modelling > Face Verification | IJB-B | Cos+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR @ FAR=0.01 | 97.36% |
Facial Recognition and Modelling > Face Verification | IJB-B | Cos+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR @ FAR=0.001 | 96.5 |
Facial Recognition and Modelling > Face Verification | IJB-B | Cos+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR@FAR=0.0001 | 94.99 |
Facial Recognition and Modelling > Face Verification | IJB-B | FPN | http://arxiv.org/abs/1708.07517v2 | TAR @ FAR=0.01 | 96.5% |
Facial Recognition and Modelling > Face Verification | IJB-B | SE-GV-3-g2 | http://arxiv.org/abs/1810.09951v1 | TAR @ FAR=0.01 | 96.4% |
Facial Recognition and Modelling > Face Verification | IJB-B | VGGFace2_ft | http://arxiv.org/abs/1710.08092v2 | TAR @ FAR=0.01 | 95.6% |
Facial Recognition and Modelling > Face Verification | IJB-B | VGGFace2_ft | http://arxiv.org/abs/1710.08092v2 | TAR @ FAR=0.001 | 90.8 |
Facial Recognition and Modelling > Face Verification | IJB-B | CAFace+AdaFace (WebFace4M) | https://arxiv.org/abs/2210.10864v3 | TAR @ FAR=0.001 | 96.91 |
Facial Recognition and Modelling > Face Verification | IJB-B | CAFace+AdaFace (WebFace4M) | https://arxiv.org/abs/2210.10864v3 | TAR@FAR=0.0001 | 95.53 |
Facial Recognition and Modelling > Face Verification | IJB-B | CAFace+AdaFace (WebFace4M) | https://arxiv.org/abs/2210.10864v3 | TAR @ FAR=1e-5 | 92.29 |
Facial Recognition and Modelling > Face Verification | IJB-B | PartialFC(WebFace42M) | https://arxiv.org/abs/2203.15565v1 | TAR@FAR=0.0001 | 96.71 |
Facial Recognition and Modelling > Face Verification | IJB-B | AdaFace (WebFace4M) | https://arxiv.org/abs/2204.00964v2 | TAR@FAR=0.0001 | 96.03 |
Facial Recognition and Modelling > Face Verification | IJB-B | AdaFace (MS1MV3) | https://arxiv.org/abs/2204.00964v2 | TAR@FAR=0.0001 | 95.84 |
Facial Recognition and Modelling > Face Verification | IJB-B | AdaFace (MS1MV2) | https://arxiv.org/abs/2204.00964v2 | TAR@FAR=0.0001 | 95.67 |
Facial Recognition and Modelling > Face Verification | CPLFW | DiscFace | https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html | Accuracy | 93.37 |
Facial Recognition and Modelling > Face Verification | CPLFW | SFace | https://arxiv.org/abs/2205.12010v1 | Accuracy | 91.05% |
Facial Recognition and Modelling > Face Verification | BTS3.1 | ProxyFusion (Adaface) | https://proceedings.neurips.cc/paper_files/paper/2024/hash/81f554467f27759e88de14ba2fbafb47-Abstract-Conference.html | TAR @ FAR=0.01 | 0.689 |
Facial Recognition and Modelling > Face Verification | BTS3.1 | CoNAN (Adaface) | https://arxiv.org/abs/2307.10237v1 | TAR @ FAR=0.01 | 0.5632 |
Facial Recognition and Modelling > Face Verification | BTS3.1 | NAN (Adaface) | http://arxiv.org/abs/1603.05474v4 | TAR @ FAR=0.01 | 0.5444 |
Facial Recognition and Modelling > Face Verification | BTS3.1 | MCN (Adaface) | http://arxiv.org/abs/1807.09192v1 | TAR @ FAR=0.01 | 0.5425 |
Facial Recognition and Modelling > Face Verification | BTS3.1 | CAFace (Adaface) | https://arxiv.org/abs/2210.10864v3 | TAR @ FAR=0.01 | 0.5131 |
Facial Recognition and Modelling > Face Verification | BTS3.1 | MCN (Arcface) | http://arxiv.org/abs/1603.05474v4 | TAR @ FAR=0.01 | 0.3941 |
Facial Recognition and Modelling > Face Verification | BTS3.1 | NAN (Arcface) | http://arxiv.org/abs/1603.05474v4 | TAR @ FAR=0.01 | 0.3901 |
Facial Recognition and Modelling > Face Verification > Disguised Face Verification | MegaFace | FaceNet | http://arxiv.org/abs/1503.03832v3 | Accuracy | 86.47 |
Facial Recognition and Modelling > Face Verification > Disguised Face Verification | Disguised Faces in the Wild | DisguiseNet | http://arxiv.org/abs/1804.09669v2 | GAR @0.1% FAR | 23.25 |
Facial Recognition and Modelling > Face Verification > Disguised Face Verification | Disguised Faces in the Wild | DisguiseNet | http://arxiv.org/abs/1804.09669v2 | GAR @1% FAR | 60.89 |
Facial Recognition and Modelling > Face Verification > Disguised Face Verification | Disguised Faces in the Wild | DisguiseNet | http://arxiv.org/abs/1804.09669v2 | GAR @10% FAR | 98.99 |
Facial Recognition and Modelling > Face Verification > Disguised Face Verification | Disguised Faces in the Wild | VGG-Face model features + cosine similarity metric | http://arxiv.org/abs/1811.08837v1 | GAR @0.1% FAR | 17.73 |
Facial Recognition and Modelling > Face Verification > Disguised Face Verification | Disguised Faces in the Wild | VGG-Face model features + cosine similarity metric | http://arxiv.org/abs/1811.08837v1 | GAR @1% FAR | 33.76 |
Facial Recognition and Modelling > Face Alignment | 3DFAW | 3D Face alignment | http://arxiv.org/abs/1609.09545v1 | CVGTCE | 3.4767% |
Facial Recognition and Modelling > Face Alignment | 3DFAW | 3D Face alignment | http://arxiv.org/abs/1609.09545v1 | GTE | 4.5623 |
Facial Recognition and Modelling > Face Alignment | AFLW | SynergyNet | https://arxiv.org/abs/2110.09772v3 | Mean NME | 4.06 |
Facial Recognition and Modelling > Face Alignment | AFLW | 3DDFA_V2 | https://arxiv.org/abs/2009.09960v2 | Mean NME | 4.43 |
Facial Recognition and Modelling > Face Alignment | AFLW | 3DDFA | http://arxiv.org/abs/1804.01005v1 | Mean NME | 4.55 |
Facial Recognition and Modelling > Face Alignment | LS3D-W Balanced | 3D-FAN | http://arxiv.org/abs/1703.07332v3 | AUC0.07 | 72.3% |
Facial Recognition and Modelling > Face Alignment | CelebA Aligned | Progressive Face SR | https://arxiv.org/abs/1908.08239v1 | MOS | 3.73 |
Facial Recognition and Modelling > Face Alignment | CelebA Aligned | Progressive Face SR | https://arxiv.org/abs/1908.08239v1 | MS-SSIM | 0.902 |
Facial Recognition and Modelling > Face Alignment | CelebA Aligned | Progressive Face SR | https://arxiv.org/abs/1908.08239v1 | PSNR | 22.66 |
Facial Recognition and Modelling > Face Alignment | CelebA Aligned | Progressive Face SR | https://arxiv.org/abs/1908.08239v1 | SSIM | 0.685 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | SH-FAN | https://arxiv.org/abs/2111.02360v1 | NME (inter-ocular) | 3.72 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | SH-FAN | https://arxiv.org/abs/2111.02360v1 | AUC@10 (inter-ocular) | 63.1 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | SH-FAN | https://arxiv.org/abs/2111.02360v1 | FR@10 (inter-ocular) | 1.55 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | FaRL-B (epoch 16) | https://arxiv.org/abs/2112.03109v3 | NME (inter-ocular) | 3.96 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | FaRL-B (epoch 16) | https://arxiv.org/abs/2112.03109v3 | AUC@10 (inter-ocular) | 61.16 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | FaRL-B (epoch 16) | https://arxiv.org/abs/2112.03109v3 | FR@10 (inter-ocular) | 1.76 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | SPIGA | https://arxiv.org/abs/2210.07233v1 | NME (inter-ocular) | 4.06 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | SPIGA | https://arxiv.org/abs/2210.07233v1 | AUC@10 (inter-ocular) | 60.56 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | SPIGA | https://arxiv.org/abs/2210.07233v1 | FR@10 (inter-ocular) | 2.08 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | HIH | https://arxiv.org/abs/2104.03100v2 | NME (inter-ocular) | 4.08 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | HIH | https://arxiv.org/abs/2104.03100v2 | AUC@10 (inter-ocular) | 60.50 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | HIH | https://arxiv.org/abs/2104.03100v2 | FR@10 (inter-ocular) | 2.60 |
Facial Recognition and Modelling > Face Alignment | WFW (Extra Data) | ADNet | https://arxiv.org/abs/2105.10697v1 | NME (inter-ocular) | 4.14 |
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