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 Recognition > Unsupervised face recognition | AgeDB-30 | USynthFace | https://arxiv.org/abs/2211.07371v1 | Accuracy | 71.62 |
Facial Recognition and Modelling > Face Swapping | HOD | Work | https://arxiv.org/abs/2311.11009v1 | 0-shot MRR | Good |
Facial Recognition and Modelling > Face Swapping | ^(#$!@#$)(()))****** | Ganesh deka | https://arxiv.org/abs/2402.04499v2 | 10% | 2 |
Facial Recognition and Modelling > Face Swapping | AFLW2000-3D | FaceDancer (Config A) | https://arxiv.org/abs/2210.10473v2 | ID retrieval | 98.5 |
Facial Recognition and Modelling > Face Swapping | AFLW2000-3D | FaceDancer (Config A) | https://arxiv.org/abs/2210.10473v2 | pose | 14.97 |
Facial Recognition and Modelling > Face Swapping | AFLW2000-3D | FaceDancer (Config A) | https://arxiv.org/abs/2210.10473v2 | exp embedding L2 | 7.07 |
Facial Recognition and Modelling > Face Swapping | AFLW2000-3D | FaceDancer (Config B) | https://arxiv.org/abs/2210.10473v2 | ID retrieval | 97.95 |
Facial Recognition and Modelling > Face Swapping | AFLW2000-3D | FaceDancer (Config B) | https://arxiv.org/abs/2210.10473v2 | pose | 5.86 |
Facial Recognition and Modelling > Face Swapping | AFLW2000-3D | FaceDancer (Config B) | https://arxiv.org/abs/2210.10473v2 | exp embedding L2 | 5.74 |
Facial Recognition and Modelling > Face Swapping | AFLW2000-3D | FaceDancer (Config C) | https://arxiv.org/abs/2210.10473v2 | ID retrieval | 97.65 |
Facial Recognition and Modelling > Face Swapping | AFLW2000-3D | FaceDancer (Config C) | https://arxiv.org/abs/2210.10473v2 | pose | 5.82 |
Facial Recognition and Modelling > Face Swapping | AFLW2000-3D | FaceDancer (Config C) | https://arxiv.org/abs/2210.10473v2 | exp embedding L2 | 4.13 |
Facial Recognition and Modelling > Face Swapping | AFLW2000-3D | FaceDancer (Config D) | https://arxiv.org/abs/2210.10473v2 | ID retrieval | 97.10 |
Facial Recognition and Modelling > Face Swapping | AFLW2000-3D | FaceDancer (Config D) | https://arxiv.org/abs/2210.10473v2 | pose | 5.75 |
Facial Recognition and Modelling > Face Swapping | AFLW2000-3D | FaceDancer (Config D) | https://arxiv.org/abs/2210.10473v2 | exp embedding L2 | 4.15 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | DeepFaceLab | https://arxiv.org/abs/2005.05535v5 | pose | 1.12 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | DeepFaceLab | https://arxiv.org/abs/2005.05535v5 | SSIM | 0.73 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | DeepFaceLab | https://arxiv.org/abs/2005.05535v5 | perceptual loss | 0.39 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | DeepFaceLab | https://arxiv.org/abs/2005.05535v5 | verification | 0.61 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | DeepFaceLab | https://arxiv.org/abs/2005.05535v5 | landmarks | 0.73 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | SimSwap-oFM | https://arxiv.org/abs/2106.06340v1 | pose | 1.22 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FaceDancer (Config C) | https://arxiv.org/abs/2210.10473v2 | ID retrieval | 98.84 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FaceDancer (Config C) | https://arxiv.org/abs/2210.10473v2 | pose | 2.04 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FaceDancer (Config C) | https://arxiv.org/abs/2210.10473v2 | exp embedding L2 | 7.97 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FaceDancer (Config D) | https://arxiv.org/abs/2210.10473v2 | ID retrieval | 98.19 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FaceDancer (Config D) | https://arxiv.org/abs/2210.10473v2 | pose | 2.15 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FaceDancer (Config D) | https://arxiv.org/abs/2210.10473v2 | exp embedding L2 | 5.70 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FIVA | https://arxiv.org/abs/2309.04228v1 | ID retrieval | 99.25 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FIVA | https://arxiv.org/abs/2309.04228v1 | pose | 2.16 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FaceDancer (Config B) | https://arxiv.org/abs/2210.10473v2 | ID retrieval | 98.54 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FaceDancer (Config B) | https://arxiv.org/abs/2210.10473v2 | pose | 2.24 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FaceDancer (Config B) | https://arxiv.org/abs/2210.10473v2 | exp embedding L2 | 8.52 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | HifiFace | https://arxiv.org/abs/2106.09965v1 | ID retrieval | 98.48 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | HifiFace | https://arxiv.org/abs/2106.09965v1 | pose | 2.63 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | MegaFS | https://arxiv.org/abs/2105.04932v2 | ID retrieval | 90.83 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | MegaFS | https://arxiv.org/abs/2105.04932v2 | pose | 2.64 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | MegaFS | https://arxiv.org/abs/2105.04932v2 | expression | 2.96 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | CihaNet | null | ID retrieval | 80.4 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | CihaNet | null | pose | 3.27 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | CihaNet | null | SSIM | 0.992 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | DeepFakes | https://arxiv.org/abs/2005.05535v5 | pose | 4.75 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | DeepFakes | https://arxiv.org/abs/2005.05535v5 | SSIM | 0.71 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | DeepFakes | https://arxiv.org/abs/2005.05535v5 | perceptual loss | 0.41 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | DeepFakes | https://arxiv.org/abs/2005.05535v5 | verification | 0.69 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | DeepFakes | https://arxiv.org/abs/2005.05535v5 | landmarks | 1.15 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | Nirkin et al. | https://arxiv.org/abs/2005.05535v5 | pose | 6.01 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | Nirkin et al. | https://arxiv.org/abs/2005.05535v5 | SSIM | 0.65 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | Nirkin et al. | https://arxiv.org/abs/2005.05535v5 | perceptual loss | 0.5 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | Nirkin et al. | https://arxiv.org/abs/2005.05535v5 | verification | 0.66 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | Nirkin et al. | https://arxiv.org/abs/2005.05535v5 | landmarks | 0.35 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | SimSwap-nFM | https://arxiv.org/abs/2106.06340v1 | ID retrieval | 96.57 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FaceController | https://arxiv.org/abs/2102.11464v1 | FID | 3.51 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FaceSwap | https://arxiv.org/abs/2102.11464v1 | FID | 3.81 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FaceShifter | https://arxiv.org/abs/2102.11464v1 | FID | 4.05 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | DeepFake | https://arxiv.org/abs/2102.11464v1 | FID | 4.29 |
Facial Recognition and Modelling > Face Swapping | FaceForensics++ | FSGAN | https://arxiv.org/abs/2102.11464v1 | FID | 4.35 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | CAER | EfficientFace | https://ojs.aaai.org/index.php/AAAI/article/view/16465 | Accuracy | 85.87 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | CREMA-D | EmoAffectNet LSTM | https://www.sciencedirect.com/science/article/abs/pii/S0925231222012656 | UAR | 79.0 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | ^(#$!@#$)(()))****** | S | https://aclanthology.org/2020.coling-main.519 | 0..5sec | sa |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | CK+ | EmoNeXt | https://link.springer.com/article/10.1007/s00521-024-10938-0 | Accuracy (8 emotion) | 100 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | CK+ | FN2EN | http://arxiv.org/abs/1609.06591v2 | Accuracy (8 emotion) | 96.8 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | CK+ | FN2EN | http://arxiv.org/abs/1609.06591v2 | Accuracy (7 emotion) | - |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | CK+ | FN2EN | http://arxiv.org/abs/1609.06591v2 | Accuracy (6 emotion) | 98.6 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | CK+ | PAtt-Lite | https://arxiv.org/abs/2306.09626v2 | Accuracy (7 emotion) | 100.00 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | CK+ | ViT + SE | https://arxiv.org/abs/2107.03107v4 | Accuracy (7 emotion) | 99.8 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | CK+ | FAN | https://arxiv.org/abs/1907.00193v2 | Accuracy (7 emotion) | 99.7 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | CK+ | Nonlinear eval on SL + SSL puzzling (B0) | https://arxiv.org/abs/2105.06421v3 | Accuracy (7 emotion) | 98.23 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | CK+ | DeepEmotion | http://arxiv.org/abs/1902.01019v1 | Accuracy (7 emotion) | 98 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Real-World Affective Faces | Covariance Pooling | http://arxiv.org/abs/1805.04855v1 | Accuracy | 87.0% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Real-World Affective Faces | Multi Label Output | https://arxiv.org/abs/2110.15028v1 | Accuracy | 79.26% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Oulu-CASIA | Dynamic MTL | https://arxiv.org/abs/1911.03281v1 | Accuracy (10-fold) | 89.6 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Oulu-CASIA | PPDN | http://arxiv.org/abs/1607.06997v2 | Accuracy (10-fold) | 84.59 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RaFD | ViT + SE | https://arxiv.org/abs/2107.03107v4 | Accuracy | 87.22 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Acted Facial Expressions In The Wild (AFEW) | ResNet50 | https://arxiv.org/abs/2012.13912v1 | Accuracy(on validation set) | 65.5% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Acted Facial Expressions In The Wild (AFEW) | LResNet50E-IR (5 models with augmentation) | https://arxiv.org/abs/2012.13912v1 | Accuracy(on validation set) | 65.5% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Acted Facial Expressions In The Wild (AFEW) | EAC | https://arxiv.org/abs/2207.10299v2 | Accuracy(on validation set) | 65.32% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Acted Facial Expressions In The Wild (AFEW) | LResNet50E-IR (1 model with augmentation) | https://arxiv.org/abs/2012.13912v1 | Accuracy(on validation set) | 63.7% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Acted Facial Expressions In The Wild (AFEW) | LResNet50E-IR (1 model) | https://arxiv.org/abs/2012.13912v1 | Accuracy(on validation set) | 61.1% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Acted Facial Expressions In The Wild (AFEW) | Multi-task EfficientNet-B0 | https://arxiv.org/abs/2103.17107 | Accuracy(on validation set) | 59.27 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Acted Facial Expressions In The Wild (AFEW) | resnet18_noisy | https://arxiv.org/abs/2008.02655v2 | Accuracy(on validation set) | 55.17% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Acted Facial Expressions In The Wild (AFEW) | resnet18 | https://arxiv.org/abs/1907.00193v2 | Accuracy(on validation set) | 51.181% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | ExpW | ResEmoteNet | https://arxiv.org/abs/2409.10545v2 | Accuracy | 75.67 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Static Facial Expressions in the Wild | Covariance Pooling | http://arxiv.org/abs/1805.04855v1 | Accuracy | 58.14% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Static Facial Expressions in the Wild | VGG-VD-16 | http://arxiv.org/abs/1610.02255v1 | Accuracy | 54.82% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | MMI | DeXpression | http://arxiv.org/abs/1509.05371v2 | Accuracy | 98.63 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | MMI | Facial Motion Prior Network | https://arxiv.org/abs/1902.08788v2 | Accuracy | 82.74% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAVDESS | EmoAffectNet LSTM | https://www.sciencedirect.com/science/article/abs/pii/S0925231222012656 | UAR | 69.7 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | SAVEE | EmoAffectNet LSTM | https://www.sciencedirect.com/science/article/abs/pii/S0925231222012656 | UAR | 82.8 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | PAtt-Lite | https://arxiv.org/abs/2306.09626v2 | Accuracy | 95.55 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | GReFEL | https://arxiv.org/abs/2410.15927v1 | Accuracy | 93.09 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | QCS | https://arxiv.org/abs/2411.01988v5 | Accuracy | 91.85 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | ResNet18 Dense Architecture | https://arxiv.org/abs/2407.04560v1 | Accuracy | 91.41 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | DDAMFN | https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=P4efBMcAAAAJ&citation_for_view=P4efBMcAAAAJ:d1gkVwhDpl0C | Accuracy | 90.74 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | KTN | https://doi.org/10.1109/TIP.2021.3049955 | Accuracy | 90.49 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | Vit-base + MAE | https://arxiv.org/abs/2207.11081v4 | Accuracy | 90.18 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | FER-VT | https://www.sciencedirect.com/science/article/pii/S0020025521008495 | Accuracy | 90.04 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | EAC | https://arxiv.org/abs/2207.10299v2 | Accuracy | 89.64 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | LResNet50E-IR | https://arxiv.org/abs/2012.13912v1 | Accuracy | 89.257 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | ViT-base | https://arxiv.org/abs/2207.11081v4 | Accuracy | 88.91 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | ViT-tiny | https://arxiv.org/abs/2207.11081v4 | Accuracy | 88.56 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.