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 | IJB-C | HeadSharing: SH-KD | https://arxiv.org/abs/2201.06945v2 | TAR @ FAR=1e-5 | 93.73% |
Facial Recognition and Modelling > Face Verification | IJB-C | HeadSharing: SH-KD | https://arxiv.org/abs/2201.06945v2 | TAR @ FAR=1e-6 | 90.24% |
Facial Recognition and Modelling > Face Verification | IJB-C | HeadSharing: SH-KD | https://arxiv.org/abs/2201.06945v2 | training dataset | MS1M V3 |
Facial Recognition and Modelling > Face Verification | IJB-C | HeadSharing: SH-KD | https://arxiv.org/abs/2201.06945v2 | model | MobileFaceNet |
Facial Recognition and Modelling > Face Verification | IJB-C | HeadSharing: TH-KD | https://arxiv.org/abs/2201.06945v2 | TAR @ FAR=1e-4 | 95.48% |
Facial Recognition and Modelling > Face Verification | IJB-C | HeadSharing: TH-KD | https://arxiv.org/abs/2201.06945v2 | TAR @ FAR=1e-5 | 93.50% |
Facial Recognition and Modelling > Face Verification | IJB-C | HeadSharing: TH-KD | https://arxiv.org/abs/2201.06945v2 | TAR @ FAR=1e-6 | 89.82% |
Facial Recognition and Modelling > Face Verification | IJB-C | HeadSharing: TH-KD | https://arxiv.org/abs/2201.06945v2 | training dataset | MS1M V3 |
Facial Recognition and Modelling > Face Verification | IJB-C | HeadSharing: TH-KD | https://arxiv.org/abs/2201.06945v2 | model | MobileFaceNet |
Facial Recognition and Modelling > Face Verification | IJB-C | ArcFace+CSFM | https://arxiv.org/abs/2207.10180v1 | TAR @ FAR=1e-4 | 95.9% |
Facial Recognition and Modelling > Face Verification | IJB-C | ArcFace+CSFM | https://arxiv.org/abs/2207.10180v1 | TAR @ FAR=1e-5 | 94.06% |
Facial Recognition and Modelling > Face Verification | IJB-C | ArcFace+CSFM | https://arxiv.org/abs/2207.10180v1 | TAR @ FAR=1e-6 | 89.34% |
Facial Recognition and Modelling > Face Verification | IJB-C | ArcFace+CSFM | https://arxiv.org/abs/2207.10180v1 | Rank-1 | 96.31 |
Facial Recognition and Modelling > Face Verification | IJB-C | ArcFace+CSFM | https://arxiv.org/abs/2207.10180v1 | Rank-5 | 97.48 |
Facial Recognition and Modelling > Face Verification | IJB-C | Partial FC | https://arxiv.org/abs/2203.15565v1 | TAR @ FAR=1e-4 | 98.00% |
Facial Recognition and Modelling > Face Verification | IJB-C | Partial FC | https://arxiv.org/abs/2203.15565v1 | TAR @ FAR=1e-5 | 97.23% |
Facial Recognition and Modelling > Face Verification | IJB-C | Partial FC | https://arxiv.org/abs/2203.15565v1 | training dataset | WebFace42M |
Facial Recognition and Modelling > Face Verification | IJB-C | Partial FC | https://arxiv.org/abs/2203.15565v1 | model | ViT-L |
Facial Recognition and Modelling > Face Verification | IJB-C | PartialFC | https://arxiv.org/abs/2203.15565v1 | TAR @ FAR=1e-4 | 97.97% |
Facial Recognition and Modelling > Face Verification | IJB-C | PartialFC | https://arxiv.org/abs/2203.15565v1 | TAR @ FAR=1e-5 | 96.93% |
Facial Recognition and Modelling > Face Verification | IJB-C | PartialFC | https://arxiv.org/abs/2203.15565v1 | training dataset | WebFace42M |
Facial Recognition and Modelling > Face Verification | IJB-C | PartialFC | https://arxiv.org/abs/2203.15565v1 | model | R200 |
Facial Recognition and Modelling > Face Verification | IJB-C | ArcFace | https://arxiv.org/abs/1801.07698v4 | TAR @ FAR=1e-5 | 96.07% |
Facial Recognition and Modelling > Face Verification | IJB-C | ArcFace | https://arxiv.org/abs/1801.07698v4 | training dataset | IBUG-500K |
Facial Recognition and Modelling > Face Verification | IJB-C | ArcFace | https://arxiv.org/abs/1801.07698v4 | model | R100 |
Facial Recognition and Modelling > Face Verification | IJB-C | Mag+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR @ FAR=1e-5 | 94.7% |
Facial Recognition and Modelling > Face Verification | IJB-C | Cos+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR @ FAR=1e-3 | 97.57 |
Facial Recognition and Modelling > Face Verification | IJB-C | Cos+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR @ FAR=1e-4 | 96.38% |
Facial Recognition and Modelling > Face Verification | IJB-C | Cos+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR @ FAR=1e-5 | 94.47% |
Facial Recognition and Modelling > Face Verification | IJB-C | Cos+UNPG | https://arxiv.org/abs/2203.11593v2 | training dataset | MS1MV2 |
Facial Recognition and Modelling > Face Verification | IJB-C | Cos+UNPG | https://arxiv.org/abs/2203.11593v2 | model | R100 |
Facial Recognition and Modelling > Face Verification | IJB-C | L2E+IS-sampling | https://ieeexplore.ieee.org/document/9607686 | TAR @ FAR=1e-3 | 97.05% |
Facial Recognition and Modelling > Face Verification | IJB-C | L2E+IS-sampling | https://ieeexplore.ieee.org/document/9607686 | TAR @ FAR=1e-4 | 95.49% |
Facial Recognition and Modelling > Face Verification | IJB-C | L2E+IS-sampling | https://ieeexplore.ieee.org/document/9607686 | TAR @ FAR=1e-5 | 93.25% |
Facial Recognition and Modelling > Face Verification | IJB-C | L2E+IS-sampling | https://ieeexplore.ieee.org/document/9607686 | training dataset | MS1M V3 |
Facial Recognition and Modelling > Face Verification | IJB-C | L2E+IS-sampling | https://ieeexplore.ieee.org/document/9607686 | model | MobileFaceNet |
Facial Recognition and Modelling > Face Verification | IJB-C | MagFace++ | https://arxiv.org/abs/2103.06627v4 | TAR @ FAR=1e-4 | 95.97% |
Facial Recognition and Modelling > Face Verification | IJB-C | MagFace++ | https://arxiv.org/abs/2103.06627v4 | TAR @ FAR=1e-5 | 90.36% |
Facial Recognition and Modelling > Face Verification | IJB-C | MagFace++ | https://arxiv.org/abs/2103.06627v4 | training dataset | MS1MV2 |
Facial Recognition and Modelling > Face Verification | IJB-C | MagFace++ | https://arxiv.org/abs/2103.06627v4 | model | R100 |
Facial Recognition and Modelling > Face Verification | IJB-C | circle loss | https://arxiv.org/abs/2002.10857v2 | TAR @ FAR=1e-3 | 96.29% |
Facial Recognition and Modelling > Face Verification | IJB-C | circle loss | https://arxiv.org/abs/2002.10857v2 | TAR @ FAR=1e-4 | 93.95% |
Facial Recognition and Modelling > Face Verification | IJB-C | circle loss | https://arxiv.org/abs/2002.10857v2 | TAR @ FAR=1e-5 | 89.60% |
Facial Recognition and Modelling > Face Verification | IJB-C | circle loss | https://arxiv.org/abs/2002.10857v2 | training dataset | MS1M Cleaned |
Facial Recognition and Modelling > Face Verification | IJB-C | circle loss | https://arxiv.org/abs/2002.10857v2 | model | R100 |
Facial Recognition and Modelling > Face Verification | IJB-C | WebFace42M baseline | https://arxiv.org/abs/2103.04098v1 | TAR @ FAR=1e-4 | 97.7% |
Facial Recognition and Modelling > Face Verification | IJB-C | WebFace42M baseline | https://arxiv.org/abs/2103.04098v1 | training dataset | WebFace42M |
Facial Recognition and Modelling > Face Verification | IJB-C | WebFace42M baseline | https://arxiv.org/abs/2103.04098v1 | model | R100 |
Facial Recognition and Modelling > Face Verification | IJB-C | AdaFace (WebFace4M) | https://arxiv.org/abs/2204.00964v2 | TAR @ FAR=1e-4 | 97.39% |
Facial Recognition and Modelling > Face Verification | IJB-C | FFC | https://arxiv.org/abs/2105.10375v5 | TAR @ FAR=1e-4 | 97.31% |
Facial Recognition and Modelling > Face Verification | IJB-C | FFC | https://arxiv.org/abs/2105.10375v5 | training dataset | WebFace42M |
Facial Recognition and Modelling > Face Verification | IJB-C | FFC | https://arxiv.org/abs/2105.10375v5 | model | R100 |
Facial Recognition and Modelling > Face Verification | IJB-C | CAFace+AdaFace (WebFace4M) | https://arxiv.org/abs/2210.10864v3 | TAR @ FAR=1e-3 | 98.08 |
Facial Recognition and Modelling > Face Verification | IJB-C | CAFace+AdaFace (WebFace4M) | https://arxiv.org/abs/2210.10864v3 | TAR @ FAR=1e-4 | 97.3% |
Facial Recognition and Modelling > Face Verification | IJB-C | AdaFace (MS1MV3) | https://arxiv.org/abs/2204.00964v2 | TAR @ FAR=1e-4 | 97.09% |
Facial Recognition and Modelling > Face Verification | IJB-C | AdaFace (MS1MV2) | https://arxiv.org/abs/2204.00964v2 | TAR @ FAR=1e-4 | 96.89% |
Facial Recognition and Modelling > Face Verification | IJB-C | ElasticFace-Cos | https://arxiv.org/abs/2109.09416v4 | TAR @ FAR=1e-4 | 96.57% |
Facial Recognition and Modelling > Face Verification | IJB-C | ElasticFace-Cos | https://arxiv.org/abs/2109.09416v4 | training dataset | MS1M V2 |
Facial Recognition and Modelling > Face Verification | IJB-C | ElasticFace-Cos | https://arxiv.org/abs/2109.09416v4 | model | R100 |
Facial Recognition and Modelling > Face Verification | IJB-C | Arc+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR @ FAR=1e-3 | 97.51 |
Facial Recognition and Modelling > Face Verification | IJB-C | Arc+UNPG | https://arxiv.org/abs/2203.11593v2 | TAR @ FAR=1e-4 | 96.33% |
Facial Recognition and Modelling > Face Verification | IJB-C | QMagFace | https://arxiv.org/abs/2111.13475v3 | TAR @ FAR=1e-2 | 98.51 |
Facial Recognition and Modelling > Face Verification | IJB-C | QMagFace | https://arxiv.org/abs/2111.13475v3 | TAR @ FAR=1e-3 | 97.62 |
Facial Recognition and Modelling > Face Verification | IJB-C | QMagFace | https://arxiv.org/abs/2111.13475v3 | TAR @ FAR=1e-4 | 96.19% |
Facial Recognition and Modelling > Face Verification | IJB-C | CurricularFace | https://arxiv.org/abs/2004.00288v1 | TAR @ FAR=1e-4 | 96.1% |
Facial Recognition and Modelling > Face Verification | IJB-C | PFEfuse + match | https://arxiv.org/abs/1904.09658v4 | TAR @ FAR=1e-2 | 97.17% |
Facial Recognition and Modelling > Face Verification | IJB-C | PFEfuse + match | https://arxiv.org/abs/1904.09658v4 | TAR @ FAR=1e-3 | 95.49% |
Facial Recognition and Modelling > Face Verification | IJB-C | PFEfuse + match | https://arxiv.org/abs/1904.09658v4 | training dataset | MS1M V2 |
Facial Recognition and Modelling > Face Verification | IJB-C | PFEfuse + match | https://arxiv.org/abs/1904.09658v4 | model | SphereFace64 |
Facial Recognition and Modelling > Face Verification | IJB-C | VGGFace2_ft | http://arxiv.org/abs/1710.08092v2 | TAR @ FAR=1e-2 | 96.7% |
Facial Recognition and Modelling > Face Verification | IJB-C | VGGFace2_ft | http://arxiv.org/abs/1710.08092v2 | TAR @ FAR=1e-3 | 92.7% |
Facial Recognition and Modelling > Face Verification | IJB-C | VGGFace2_ft | http://arxiv.org/abs/1710.08092v2 | training dataset | Vggface2 |
Facial Recognition and Modelling > Face Verification | IJB-C | VGGFace2_ft | http://arxiv.org/abs/1710.08092v2 | model | R50 |
Facial Recognition and Modelling > Face Verification | IJB-C | AIM | http://arxiv.org/abs/1809.00338v2 | TAR @ FAR=1e-2 | 93.5% |
Facial Recognition and Modelling > Face Verification | IJB-C | MN-vc | http://arxiv.org/abs/1807.09192v1 | TAR @ FAR=1e-2 | 92.70% |
Facial Recognition and Modelling > Face Verification | IJB-C | FaceNet | http://arxiv.org/abs/1503.03832v3 | TAR @ FAR=1e-2 | 66.5% |
Facial Recognition and Modelling > Face Verification | YouTube Faces DB | SeqFace, 1 ResNet-64 | http://arxiv.org/abs/1803.06524v2 | Accuracy | 98.12% |
Facial Recognition and Modelling > Face Verification | YouTube Faces DB | ArcFace + MS1MV2 + R100, | https://arxiv.org/abs/1801.07698v4 | Accuracy | 98.02% |
Facial Recognition and Modelling > Face Verification | YouTube Faces DB | CosFace | http://arxiv.org/abs/1801.09414v2 | Accuracy | 97.6% |
Facial Recognition and Modelling > Face Verification | YouTube Faces DB | VGG-Face | https://www.robots.ox.ac.uk/~vgg/publications/2015/Parkhi15/ | Accuracy | 97.40% |
Facial Recognition and Modelling > Face Verification | YouTube Faces DB | PFEfuse+match | https://arxiv.org/abs/1904.09658v4 | Accuracy | 97.36% |
Facial Recognition and Modelling > Face Verification | YouTube Faces DB | QAN | http://arxiv.org/abs/1704.03373v1 | Accuracy | 96.17% |
Facial Recognition and Modelling > Face Verification | YouTube Faces DB | Light CNN-29 | http://arxiv.org/abs/1511.02683v4 | Accuracy | 95.54% |
Facial Recognition and Modelling > Face Verification | YouTube Faces DB | Git Loss | http://arxiv.org/abs/1807.08512v4 | Accuracy | 95.30% |
Facial Recognition and Modelling > Face Verification | YouTube Faces DB | FaceNet | http://arxiv.org/abs/1503.03832v3 | Accuracy | 95.12% |
Facial Recognition and Modelling > Face Verification | YouTube Faces DB | SphereFace | http://arxiv.org/abs/1704.08063v4 | Accuracy | 95.0% |
Facial Recognition and Modelling > Face Verification | YouTube Faces DB | DeepId2+ | https://arxiv.org/abs/1412.1265v1 | Accuracy | 93.2% |
Facial Recognition and Modelling > Face Verification | YouTube Faces DB | 3DMM face shape parameters + CNN | http://arxiv.org/abs/1612.04904v1 | Accuracy | 88.80% |
Facial Recognition and Modelling > Face Verification | Oulu-CASIA NIR-VIS | LightCNN-29 + DVG | https://arxiv.org/abs/1903.10203v3 | TAR @ FAR=0.001 | 92.9 |
Facial Recognition and Modelling > Face Verification | Oulu-CASIA NIR-VIS | LightCNN-29 + DVG | https://arxiv.org/abs/1903.10203v3 | TAR @ FAR=0.01 | 98.5 |
Facial Recognition and Modelling > Face Verification | Oulu-CASIA NIR-VIS | DVR Wu et al. (2019) | http://arxiv.org/abs/1809.01936v3 | TAR @ FAR=0.001 | 84.9 |
Facial Recognition and Modelling > Face Verification | Oulu-CASIA NIR-VIS | DVR Wu et al. (2019) | http://arxiv.org/abs/1809.01936v3 | TAR @ FAR=0.01 | 97.2 |
Facial Recognition and Modelling > Face Verification | Oulu-CASIA NIR-VIS | W-CNN He et al. (2018) | http://arxiv.org/abs/1708.02412v1 | TAR @ FAR=0.001 | 54.6 |
Facial Recognition and Modelling > Face Verification | Oulu-CASIA NIR-VIS | W-CNN He et al. (2018) | http://arxiv.org/abs/1708.02412v1 | TAR @ FAR=0.01 | 81.5 |
Facial Recognition and Modelling > Face Verification | MegaFace | Prodpoly | https://arxiv.org/abs/2006.13026v2 | Accuracy | 98.95% |
Facial Recognition and Modelling > Face Verification | MegaFace | ElasticFace-Arc | https://arxiv.org/abs/2109.09416v4 | Accuracy | 98.81% |
Facial Recognition and Modelling > Face Verification | MegaFace | GhostFaceNetV2-1 | https://ieeexplore.ieee.org/document/10098610 | Accuracy | 98.72% |
Facial Recognition and Modelling > Face Verification | MegaFace | ArcFace + MS1MV2 + R100 + R | https://arxiv.org/abs/1801.07698v4 | Accuracy | 98.48% |
Facial Recognition and Modelling > Face Verification | MegaFace | DiscFace | https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html | Accuracy | 97.44% |
Facial Recognition and Modelling > Face Verification | MegaFace | Dynamic AdaCos | https://arxiv.org/abs/1905.00292v2 | Accuracy | 97.41% |
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