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 Detection | WIDER Face (Hard) | ACF-WIDER | https://arxiv.org/abs/1407.4023v2 | AP | 0.290 |
Facial Recognition and Modelling > Face Detection > Occluded Face Detection | WIDER Face (Easy) | TinaFace(ResNet-50) | https://arxiv.org/abs/2011.13183v3 | AP | 0.97 |
Facial Recognition and Modelling > Face Detection > Occluded Face Detection | WIDER Face (Medium) | TinaFace(ResNet-50) | https://arxiv.org/abs/2011.13183v3 | AP | 0.963 |
Facial Recognition and Modelling > Face Detection > Occluded Face Detection | MAFA | FAN | http://arxiv.org/abs/1711.07246v2 | MAP | 88.3% |
Facial Recognition and Modelling > Face Detection > Occluded Face Detection | MAFA | AOFD | http://arxiv.org/abs/1709.05188v6 | MAP | 77.3% |
Facial Recognition and Modelling > Face Detection > Occluded Face Detection | WIDER Face (Hard) | TinaFace(ResNet-50) | https://arxiv.org/abs/2011.13183v3 | AP | 0.934 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 8-shot learning | CainGAN | https://arxiv.org/abs/2004.09169v1 | FID | 24.9 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 8-shot learning | Few-shot Adversarial Model | https://arxiv.org/abs/1905.08233v2 | FID | 42.2 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | Fast Bi-layer Avatars (medium size) | https://arxiv.org/abs/2008.10174v1 | LPIPS | 0.358 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | Fast Bi-layer Avatars (medium size) | https://arxiv.org/abs/2008.10174v1 | SSIM | 0.508 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | Fast Bi-layer Avatars (medium size) | https://arxiv.org/abs/2008.10174v1 | CSIM | 0.653 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | Fast Bi-layer Avatars (medium size) | https://arxiv.org/abs/2008.10174v1 | Normalized Pose Error | 43.3 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | Fast Bi-layer Avatars (medium size) | https://arxiv.org/abs/2008.10174v1 | inference time (ms) | 4 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | First Order Motion Model (medium size) | https://arxiv.org/abs/2008.10174v1 | LPIPS | 0.311 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | First Order Motion Model (medium size) | https://arxiv.org/abs/2008.10174v1 | SSIM | 0.553 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | First Order Motion Model (medium size) | https://arxiv.org/abs/2008.10174v1 | CSIM | 0.638 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | First Order Motion Model (medium size) | https://arxiv.org/abs/2008.10174v1 | Normalized Pose Error | 47.8 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | First Order Motion Model (medium size) | https://arxiv.org/abs/2008.10174v1 | inference time (ms) | 13 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | Few-shot Vid-to-vid (medium size) | https://arxiv.org/abs/2008.10174v1 | LPIPS | 0.368 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | Few-shot Vid-to-vid (medium size) | https://arxiv.org/abs/2008.10174v1 | SSIM | 0.419 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | Few-shot Vid-to-vid (medium size) | https://arxiv.org/abs/2008.10174v1 | CSIM | 0.604 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | Few-shot Vid-to-vid (medium size) | https://arxiv.org/abs/2008.10174v1 | Normalized Pose Error | 46.1 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | Few-shot Vid-to-vid (medium size) | https://arxiv.org/abs/2008.10174v1 | inference time (ms) | 22 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | CainGAN | https://arxiv.org/abs/2004.09169v1 | FID | 35.0 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 1-shot learning | Few-shot Adversarial Model | https://arxiv.org/abs/1905.08233v2 | FID | 48.5 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | 100 sleep nights of 8 caregivers | Ashok | https://arxiv.org/abs/1912.06078v1 | 10% | 12 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb1 - 8-shot learning | Few-shot Adversarial Model | https://arxiv.org/abs/1905.08233v2 | FID | 38.0 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb1 - 8-shot learning | X2Face | http://openaccess.thecvf.com/content_ECCV_2018/html/Olivia_Wiles_X2Face_A_network_ECCV_2018_paper.html | FID | 51.5 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb1 - 32-shot learning | Few-shot Adversarial Model | https://arxiv.org/abs/1905.08233v2 | FID | 29.5 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb1 - 32-shot learning | X2Face | http://openaccess.thecvf.com/content_ECCV_2018/html/Olivia_Wiles_X2Face_A_network_ECCV_2018_paper.html | FID | 56.5 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb1 - 1-shot learning | Few-shot Adversarial Model | https://arxiv.org/abs/1905.08233v2 | FID | 43.0 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb1 - 1-shot learning | X2Face | http://openaccess.thecvf.com/content_ECCV_2018/html/Olivia_Wiles_X2Face_A_network_ECCV_2018_paper.html | FID | 45.8 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation | VoxCeleb2 - 32-shot learning | Few-shot Adversarial Model | https://arxiv.org/abs/1905.08233v2 | FID | 30.6 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS2 | Wav2Lip + ViT + MARLIN | https://arxiv.org/abs/2211.06627v3 | LSE-D | 7.127 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS2 | Wav2Lip + ViT + MARLIN | https://arxiv.org/abs/2211.06627v3 | LSE-C | 5.528 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS2 | Wav2Lip + ViT + MARLIN | https://arxiv.org/abs/2211.06627v3 | FID | 3.452 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS2 | Wav2Lip + GAN | https://arxiv.org/abs/2008.10010v1 | LSE-D | 6.469 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS2 | Wav2Lip + GAN | https://arxiv.org/abs/2008.10010v1 | FID | 4.446 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS2 | Wav2Lip | https://arxiv.org/abs/2008.10010v1 | LSE-D | 6.386 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS2 | Wav2Lip | https://arxiv.org/abs/2008.10010v1 | LSE-C | 7.781 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS2 | Wav2Lip | https://arxiv.org/abs/2008.10010v1 | FID | 4.887 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS3 | Wav2Lip + GAN | https://arxiv.org/abs/2008.10010v1 | LSE-D | 6.986 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS3 | Wav2Lip + GAN | https://arxiv.org/abs/2008.10010v1 | LSE-C | 7.574 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS3 | Wav2Lip + GAN | https://arxiv.org/abs/2008.10010v1 | FID | 4.35 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS3 | Wav2Lip | https://arxiv.org/abs/2008.10010v1 | LSE-D | 6.652 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS3 | Wav2Lip | https://arxiv.org/abs/2008.10010v1 | LSE-C | 7.887 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRS3 | Wav2Lip | https://arxiv.org/abs/2008.10010v1 | FID | 4.844 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRW | Wav2Lip + GAN | https://arxiv.org/abs/2008.10010v1 | LSE-D | 6.774 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRW | Wav2Lip + GAN | https://arxiv.org/abs/2008.10010v1 | LSE-C | 7.263 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRW | Wav2Lip + GAN | https://arxiv.org/abs/2008.10010v1 | FID | 2.475 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRW | Wav2Lip | https://arxiv.org/abs/2008.10010v1 | LSE-D | 6.512 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRW | Wav2Lip | https://arxiv.org/abs/2008.10010v1 | LSE-C | 7.49 |
Facial Recognition and Modelling > Face Generation > Talking Head Generation > Unconstrained Lip-synchronization | LRW | Wav2Lip | https://arxiv.org/abs/2008.10010v1 | FID | 3.189 |
Facial Recognition and Modelling > Face Generation > Talking Face Generation | CREMA-D | EmoGen | https://arxiv.org/abs/2303.11548v2 | EmoAcc | 83.2 |
Facial Recognition and Modelling > Face Generation > Talking Face Generation | CREMA-D | EmoGen | https://arxiv.org/abs/2303.11548v2 | FID | 5.29 |
Facial Recognition and Modelling > Face Generation > Talking Face Generation | CREMA-D | EmoGen | https://arxiv.org/abs/2303.11548v2 | LSE-C | 6.663 |
Facial Recognition and Modelling > Face Generation > Talking Face Generation | LRW | LipGAN | https://arxiv.org/abs/2003.00418v1 | LMD | 0.60 |
Facial Recognition and Modelling > Face Generation > Talking Face Generation | LRW | LipGAN | https://arxiv.org/abs/2003.00418v1 | SSIM | 0.96 |
Facial Recognition and Modelling > Face Verification | AgeDB-30 | PartialFC(R200) | https://arxiv.org/abs/2203.15565v1 | Accuracy | 0.9870 |
Facial Recognition and Modelling > Face Verification | AgeDB-30 | GhostFaceNetV2-1 | https://ieeexplore.ieee.org/document/10098610 | Accuracy | 0.9862 |
Facial Recognition and Modelling > Face Verification | AgeDB-30 | DiscFace | https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html | Accuracy | 0.9835 |
Facial Recognition and Modelling > Face Verification | AgeDB-30 | VarGFaceNet | https://arxiv.org/abs/1910.04985v4 | Accuracy | 0.9815 |
Facial Recognition and Modelling > Face Verification | AgeDB-30 | VarGNet | https://arxiv.org/abs/1907.05653v2 | Accuracy | 0.97333 |
Facial Recognition and Modelling > Face Verification | IJB-A | Dual-Agent GANs | http://papers.nips.cc/paper/6612-dual-agent-gans-for-photorealistic-and-identity-preserving-profile-face-synthesis | TAR @ FAR=0.01 | 97.60% |
Facial Recognition and Modelling > Face Verification | IJB-A | PFEfuse + match | https://arxiv.org/abs/1904.09658v4 | TAR @ FAR=0.01 | 97.5% |
Facial Recognition and Modelling > Face Verification | IJB-A | PFEfuse + match | https://arxiv.org/abs/1904.09658v4 | TAR @ FAR=0.001 | 95.25 |
Facial Recognition and Modelling > Face Verification | IJB-A | SE-GV-4-g1 | http://arxiv.org/abs/1810.09951v1 | TAR @ FAR=0.01 | 97.2% |
Facial Recognition and Modelling > Face Verification | IJB-A | L2-constrained softmax loss | http://arxiv.org/abs/1703.09507v3 | TAR @ FAR=0.01 | 97% |
Facial Recognition and Modelling > Face Verification | IJB-A | VGGFace2_ft | http://arxiv.org/abs/1710.08092v2 | TAR @ FAR=0.01 | 96.8% |
Facial Recognition and Modelling > Face Verification | IJB-A | VGGFace2_ft | http://arxiv.org/abs/1710.08092v2 | TAR @ FAR=0.001 | 92.1 |
Facial Recognition and Modelling > Face Verification | IJB-A | VGGFace2_ft | http://arxiv.org/abs/1710.08092v2 | TAR @ FAR=0.1 | 0.99 |
Facial Recognition and Modelling > Face Verification | IJB-A | StyleFNM | https://arxiv.org/abs/2312.14544v1 | TAR @ FAR=0.01 | 94.60% |
Facial Recognition and Modelling > Face Verification | IJB-A | Deep Residual Equivariant Mapping | http://arxiv.org/abs/1803.00839v1 | TAR @ FAR=0.01 | 94.40% |
Facial Recognition and Modelling > Face Verification | IJB-A | NAN | http://arxiv.org/abs/1603.05474v4 | TAR @ FAR=0.01 | 94.10% |
Facial Recognition and Modelling > Face Verification | IJB-A | Template adaptation | http://arxiv.org/abs/1603.03958v3 | TAR @ FAR=0.01 | 93.90% |
Facial Recognition and Modelling > Face Verification | IJB-A | All-in-one CNN | http://arxiv.org/abs/1611.00851v1 | TAR @ FAR=0.01 | 92.20% |
Facial Recognition and Modelling > Face Verification | IJB-A | FPN | http://arxiv.org/abs/1708.07517v2 | TAR @ FAR=0.01 | 90.1% |
Facial Recognition and Modelling > Face Verification | IJB-A | Triplet probabilistic embedding | http://arxiv.org/abs/1604.05417v3 | TAR @ FAR=0.01 | 90% |
Facial Recognition and Modelling > Face Verification | IJB-A | Synthesis as data augmentation | http://arxiv.org/abs/1603.07057v2 | TAR @ FAR=0.01 | 88.60% |
Facial Recognition and Modelling > Face Verification | IJB-A | DCNN | http://arxiv.org/abs/1508.01722v2 | TAR @ FAR=0.01 | 83.80% |
Facial Recognition and Modelling > Face Verification | IJB-A | Deep multi-pose representations | http://arxiv.org/abs/1603.07388v1 | TAR @ FAR=0.01 | 78.70% |
Facial Recognition and Modelling > Face Verification | IJB-A | Deep CNN + COTS matcher | http://arxiv.org/abs/1507.07242v2 | TAR @ FAR=0.01 | 73.30% |
Facial Recognition and Modelling > Face Verification | IJB-A | VGG + GANFaces | http://arxiv.org/abs/1804.03675v1 | TAR @ FAR=0.01 | 53.507% |
Facial Recognition and Modelling > Face Verification | IJB-A | VGG + GANFaces | http://arxiv.org/abs/1804.03675v1 | TAR @ FAR=0.001 | 18.768 |
Facial Recognition and Modelling > Face Verification | CFP-FP | PartialFC (R200) | https://arxiv.org/abs/2203.15565v1 | Accuracy | 0.9951 |
Facial Recognition and Modelling > Face Verification | CFP-FP | QMagFace | https://arxiv.org/abs/2111.13475v3 | Accuracy | 0.9874 |
Facial Recognition and Modelling > Face Verification | CFP-FP | VarGFaceNet | https://arxiv.org/abs/1910.04985v4 | Accuracy | 0.985 |
Facial Recognition and Modelling > Face Verification | CFP-FP | VarGNet | https://arxiv.org/abs/1907.05653v2 | Accuracy | 0.89829 |
Facial Recognition and Modelling > Face Verification | CALFW | DiscFace | https://openaccess.thecvf.com/content/ACCV2020/html/Kim_DiscFace_Minimum_Discrepancy_Learning_for_Deep_Face_Recognition_ACCV_2020_paper.html | Accuracy | 96.15 |
Facial Recognition and Modelling > Face Verification | CALFW | SFace | https://arxiv.org/abs/2205.12010v1 | Accuracy | 93.95% |
Facial Recognition and Modelling > Face Verification | LFW | ArcFaceR50 + EM-FRR | https://arxiv.org/abs/2210.13664v3 | FRR@FAR(%) | 0.100 |
Facial Recognition and Modelling > Face Verification | LFW | ArcFaceR50 + EM-FRR | https://arxiv.org/abs/2210.13664v3 | BFRR | 5.89 |
Facial Recognition and Modelling > Face Verification | LFW | ArcFaceR50 + EM-FRR | https://arxiv.org/abs/2210.13664v3 | BFAR | 33.65 |
Facial Recognition and Modelling > Face Verification | LFW | ArcFaceR50 + EM-C | https://arxiv.org/abs/2210.13664v3 | FRR@FAR(%) | 0.164 |
Facial Recognition and Modelling > Face Verification | LFW | ArcFaceR50 + EM-C | https://arxiv.org/abs/2210.13664v3 | BFRR | 9.18 |
Facial Recognition and Modelling > Face Verification | LFW | ArcFaceR50 + EM-C | https://arxiv.org/abs/2210.13664v3 | BFAR | 2.44 |
Facial Recognition and Modelling > Face Verification | LFW | ArcFaceR50 + EM-FAR | https://arxiv.org/abs/2210.13664v3 | FRR@FAR(%) | 0.151 |
Facial Recognition and Modelling > Face Verification | LFW | ArcFaceR50 + EM-FAR | https://arxiv.org/abs/2210.13664v3 | BFRR | 11.22 |
Facial Recognition and Modelling > Face Verification | LFW | ArcFaceR50 + EM-FAR | https://arxiv.org/abs/2210.13664v3 | BFAR | 2.11 |
Facial Recognition and Modelling > Face Verification | IJB-C | HeadSharing: SH-KD | https://arxiv.org/abs/2201.06945v2 | TAR @ FAR=1e-4 | 95.64% |
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