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 Action Unit Detection | DISFA | MDHRM | https://arxiv.org/abs/2404.06443v1 | Average F1 | 66.2 |
Facial Recognition and Modelling > Facial Action Unit Detection | DISFA | Multi-dimensional Edge Feature-based AU Relation Graph (ResNet 50) | https://arxiv.org/abs/2205.01782v2 | Average F1 | 63.1 |
Facial Recognition and Modelling > Facial Action Unit Detection | DISFA | Multi-dimensional Edge Feature-based AU Relation Graph (ResNet 50) | https://arxiv.org/abs/2205.01782v2 | Average AUC | 92.9 |
Facial Recognition and Modelling > Facial Action Unit Detection | DISFA | Multi-dimensional Edge Feature-based AU Relation Graph (Swin-B) | https://arxiv.org/abs/2205.01782v2 | Average F1 | 62.4 |
Facial Recognition and Modelling > Facial Action Unit Detection | DISFA | Multi-dimensional Edge Feature-based AU Relation Graph (Swin-B) | https://arxiv.org/abs/2205.01782v2 | Average AUC | 92.1 |
Facial Recognition and Modelling > Facial Action Unit Detection | DISFA | JAA-Net | http://arxiv.org/abs/1803.05588v2 | Average F1 | 56.0 |
Facial Recognition and Modelling > Facial Action Unit Detection | DISFA | DRML | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhao_Deep_Region_and_CVPR_2016_paper.html | Average F1 | 26.7 |
Facial Recognition and Modelling > Facial Action Unit Detection | DISFA | DRML | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhao_Deep_Region_and_CVPR_2016_paper.html | Average AUC | 52.3 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D+ | FMAE-IAT | https://arxiv.org/abs/2407.11243v2 | Average F1 | 66.8 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D+ | Norface | https://arxiv.org/abs/2407.15617v1 | Average F1 | 66.7 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D+ | FMAE | https://arxiv.org/abs/2407.11243v2 | Average F1 | 66.2 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | FMAE-IAT | https://arxiv.org/abs/2407.11243v2 | Average F1 | 67.1 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | FMAE | https://arxiv.org/abs/2407.11243v2 | Average F1 | 66.6 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | MDHRD | https://arxiv.org/abs/2404.06443v1 | Average F1 | 66.6 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | Multi-dimensional Edge Feature-based AU Relation Graph (Swin-B) | https://arxiv.org/abs/2205.01782v2 | Average F1 | 65.5 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | Multi-dimensional Edge Feature-based AU Relation Graph (Swin-B) | https://arxiv.org/abs/2205.01782v2 | Average AUC | 83.1 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | Multi-dimensional Edge Feature-based AU Relation Graph (ResNet 50) | https://arxiv.org/abs/2205.01782v2 | Average F1 | 64.7 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | Multi-dimensional Edge Feature-based AU Relation Graph (ResNet 50) | https://arxiv.org/abs/2205.01782v2 | Average AUC | 82.6 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | Multi-View Dynamic Facial Action Unit Detection | http://arxiv.org/abs/1704.07863v2 | Average F1 | 63.0 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | Swin-B | https://arxiv.org/abs/2205.01782v2 | Average F1 | 62.6 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | JAA-Net | http://arxiv.org/abs/1803.05588v2 | Average F1 | 60.0 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | ResNet 50 | https://arxiv.org/abs/2205.01782v2 | Average F1 | 59.1 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | DRML | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhao_Deep_Region_and_CVPR_2016_paper.html | Average F1 | 48.3 |
Facial Recognition and Modelling > Facial Action Unit Detection | BP4D | DRML | http://openaccess.thecvf.com/content_cvpr_2016/html/Zhao_Deep_Region_and_CVPR_2016_paper.html | Average AUC | 56.0 |
Facial Recognition and Modelling > Gender Prediction | AgeDB | MiVOLO-D1 | https://arxiv.org/abs/2307.04616v2 | Accuracy | 98.3 |
Facial Recognition and Modelling > Gender Prediction | LAGENDA | MiVOLO-V2 | https://arxiv.org/abs/2403.02302v4 | Accuracy | 97.99 |
Facial Recognition and Modelling > Gender Prediction | LAGENDA | MiVOLO-D1 | https://arxiv.org/abs/2307.04616v2 | Accuracy | 97.36 |
Facial Recognition and Modelling > Gender Prediction | FotW Gender | PAENet | https://dl.acm.org/doi/10.1145/3323873.3325053 | Accuracy (%) | 92.93 |
Facial Recognition and Modelling > Gender Prediction | FotW Gender | SIAT MMLAB | https://ieeexplore.ieee.org/document/7789583 | Accuracy (%) | 92.69 |
Facial Recognition and Modelling > Facial Attribute Classification | DiveFace | Neighbour Learning | https://arxiv.org/abs/2208.08382v1 | Accuracy (%) | 98.60 |
Facial Recognition and Modelling > Facial Attribute Classification | UTKFace | Neighbour Learning | https://arxiv.org/abs/2208.08382v1 | Accuracy (%) | 94.76 |
Facial Recognition and Modelling > Facial Attribute Classification | CelebV-HQ | MARLIN | https://arxiv.org/abs/2211.06627v3 | Accuracy | 93.9 |
Facial Recognition and Modelling > Facial Attribute Classification | CelebV-HQ | MARLIN | https://arxiv.org/abs/2211.06627v3 | AUC | 0.9561 |
Facial Recognition and Modelling > Facial Attribute Classification | FairFace | MiVOLO-V2 | https://arxiv.org/abs/2403.02302v4 | gender-top1 | 97.5 |
Facial Recognition and Modelling > Facial Attribute Classification | FairFace | MiVOLO-V2 | https://arxiv.org/abs/2403.02302v4 | age-top1 | 62.28 |
Facial Recognition and Modelling > Facial Attribute Classification | FairFace | MiVOLO-D1 | https://arxiv.org/abs/2307.04616v2 | gender-top1 | 95.73 |
Facial Recognition and Modelling > Facial Attribute Classification | FairFace | MiVOLO-D1 | https://arxiv.org/abs/2307.04616v2 | age-top1 | 61.07 |
Facial Recognition and Modelling > Facial Attribute Classification | FairFace | FairFace | https://arxiv.org/abs/1908.04913v1 | race-top1 | 93.7 |
Facial Recognition and Modelling > Facial Attribute Classification | FairFace | FairFace | https://arxiv.org/abs/1908.04913v1 | gender-top1 | 94.2 |
Facial Recognition and Modelling > Facial Attribute Classification | FairFace | FairFace | https://arxiv.org/abs/1908.04913v1 | age-top1 | 59.7 |
Facial Recognition and Modelling > Facial Attribute Classification | MORPH | Neighbour Learning | https://arxiv.org/abs/2208.08382v1 | Accuracy (%) | 96.41 |
Facial Recognition and Modelling > Facial Attribute Classification | LFWA | Label2Label | https://arxiv.org/abs/2207.08677v1 | Error Rate | 12.49 |
Facial Recognition and Modelling > Facial Attribute Classification | LFWA | SSP + SSG | http://arxiv.org/abs/1704.08740v1 | Error Rate | 12.87 |
Facial Recognition and Modelling > Facial Attribute Classification | LFWA | SSPL | http://openaccess.thecvf.com//content/CVPR2021/html/Shu_Learning_Spatial-Semantic_Relationship_for_Facial_Attribute_Recognition_With_Limited_Labeled_CVPR_2021_paper.html | Error Rate | 13.47 |
Facial Recognition and Modelling > Facial Attribute Classification | LFWA | MCNN-AUX | http://arxiv.org/abs/1604.07360v1 | Error Rate | 13.69 |
Facial Recognition and Modelling > Facial Attribute Classification | LFWA | DMTL | http://arxiv.org/abs/1706.00906v3 | Error Rate | 13.85 |
Facial Recognition and Modelling > Facial Attribute Classification | LFWA | LNets+ANet | https://arxiv.org/abs/1411.7766v3 | Error Rate | 16.15 |
Facial Recognition and Modelling > Facial Attribute Classification | LFWA | PANDA | http://arxiv.org/abs/1311.5591v2 | Error Rate | 18.97 |
Facial Recognition and Modelling > Facial Attribute Classification | bFFHQ | DebiAN | https://arxiv.org/abs/2207.10077v2 | Bias-Conflicting Accuracy | 62.8 |
Facial Recognition and Modelling > Facial Attribute Classification | bFFHQ | DCWP | https://arxiv.org/abs/2210.05247v3 | Bias-Conflicting Accuracy | 60.35 |
Facial Recognition and Modelling > Facial Attribute Classification | bFFHQ | BiaSwap | https://arxiv.org/abs/2108.10008v1 | Bias-Conflicting Accuracy | 58.87 |
Facial Recognition and Modelling > Action Unit Detection | BP4D | AU R-CNN | https://arxiv.org/abs/1812.05788v2 | Avg F1 | 63.1 |
Facial Recognition and Modelling > Age And Gender Classification | BN-AuthProf | Multinomial Naive Bayes (MNB) | https://arxiv.org/abs/2412.02058v1 | F1 score | 0.905 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Gender | MiVOLO-V2 | https://arxiv.org/abs/2403.02302v4 | Accuracy (5-fold) | 97.39 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Gender | ViT-hSeq | https://arxiv.org/abs/2403.12483v2 | Accuracy (5-fold) | 96.56 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Gender | MiVOLO-D1 | https://arxiv.org/abs/2307.04616v2 | Accuracy (5-fold) | 96.51 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Gender | RetinaFace + ArcFace + MLP + Skip connections | https://arxiv.org/abs/2108.08186v2 | Accuracy (5-fold) | 90.66 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Gender | CPG (single crop, pytorch) | https://arxiv.org/abs/1910.06562v3 | Accuracy (5-fold) | 89.66 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Gender | PAENet (single crop, tensorflow) | https://dl.acm.org/doi/10.1145/3323873.3325053 | Accuracy (5-fold) | 89.08 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Gender | Levi_Hassner CNN ( over-sample, caffe) | https://talhassner.github.io/home/publication/2015_CVPR | Accuracy (5-fold) | 86.8 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Gender | Levi_Hassner CNN (single crop, caffe) | https://talhassner.github.io/home/publication/2015_CVPR | Accuracy (5-fold) | 85.9 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Gender | LMTCNN-2-1 (single crop, tensorflow) | http://arxiv.org/abs/1806.02023v1 | Accuracy (5-fold) | 85.16 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Gender | Levi_Hassner CNN (single crop, tensorflow) | https://talhassner.github.io/home/publication/2015_CVPR | Accuracy (5-fold) | 82.52 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | ViT-hSeq | https://arxiv.org/abs/2403.12483v2 | Accuracy (5-fold) | 84.91 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | MiVOLO-V2 | https://arxiv.org/abs/2403.02302v4 | Accuracy (5-fold) | 69.43 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | MiVOLO-D1 | https://arxiv.org/abs/2307.04616v2 | Accuracy (5-fold) | 68.69 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | AL-ResNets-34 + IMDB-WIKI | https://arxiv.org/abs/1805.10445v2 | Accuracy (5-fold) | 67.47 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | R-SAAFc2 +IMDB-WIKI | http://proceedings.mlr.press/v54/hou17a.html | Accuracy (5-fold) | 67.3 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | RoR-34 + IMDB-WIKI | http://arxiv.org/abs/1710.02985v1 | Accuracy (5-fold) | 66.74 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | MWR | https://arxiv.org/abs/2203.13122v1 | Accuracy (5-fold) | 62.6 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | UNIORD-ResNet-101 (single crop, pytorch) | https://arxiv.org/abs/2011.07607v2 | Accuracy (5-fold) | 61 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | RetinaFace + ArcFace + MLP + IC + Skip connections | https://arxiv.org/abs/2108.08186v2 | Accuracy (5-fold) | 60.86 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | CPG (single crop, pytorch) | https://arxiv.org/abs/1910.06562v3 | Accuracy (5-fold) | 57.66 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | PAENet (single crop, tensorflow) | https://dl.acm.org/doi/10.1145/3323873.3325053 | Accuracy (5-fold) | 57.3 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | MegaAge | http://arxiv.org/abs/1708.09687v2 | Accuracy (5-fold) | 56.01 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | Levi_Hassner CNN (over-sample, caffe) | https://talhassner.github.io/home/publication/2015_CVPR | Accuracy (5-fold) | 50.7 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | Levi_Hassner CNN (single crop, caffe) | https://talhassner.github.io/home/publication/2015_CVPR | Accuracy (5-fold) | 49.5 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | LMTCNN-2-1 (single crop, tensorflow) | http://arxiv.org/abs/1806.02023v1 | Accuracy (5-fold) | 44.26 |
Facial Recognition and Modelling > Age And Gender Classification | Adience Age | Levi_Hassner CNN (single crop, tensorflow) | https://talhassner.github.io/home/publication/2015_CVPR | Accuracy (5-fold) | 44.14 |
Facial Recognition and Modelling > Face Hallucination | FFHQ 512 x 512 - 16x upscaling | HiFaceGAN | https://arxiv.org/abs/2005.05005v2 | FID | 11.389 |
Facial Recognition and Modelling > Face Hallucination | FFHQ 512 x 512 - 16x upscaling | HiFaceGAN | https://arxiv.org/abs/2005.05005v2 | LPIPS | 0.2449 |
Facial Recognition and Modelling > Face Hallucination | FFHQ 512 x 512 - 16x upscaling | HiFaceGAN | https://arxiv.org/abs/2005.05005v2 | NIQE | 6.767 |
Facial Recognition and Modelling > Face Hallucination | FFHQ 512 x 512 - 16x upscaling | ESRGAN | http://arxiv.org/abs/1809.00219v2 | FID | 50.901 |
Facial Recognition and Modelling > Face Hallucination | FFHQ 512 x 512 - 16x upscaling | ESRGAN | http://arxiv.org/abs/1809.00219v2 | LPIPS | 0.3928 |
Facial Recognition and Modelling > Face Hallucination | FFHQ 512 x 512 - 16x upscaling | ESRGAN | http://arxiv.org/abs/1809.00219v2 | NIQE | 15.383 |
Facial Recognition and Modelling > Face Hallucination | FFHQ 512 x 512 - 16x upscaling | WaveletCNN | http://openaccess.thecvf.com/content_iccv_2017/html/Huang_Wavelet-SRNet_A_Wavelet-Based_ICCV_2017_paper.html | FID | 60.916 |
Facial Recognition and Modelling > Face Hallucination | FFHQ 512 x 512 - 16x upscaling | WaveletCNN | http://openaccess.thecvf.com/content_iccv_2017/html/Huang_Wavelet-SRNet_A_Wavelet-Based_ICCV_2017_paper.html | LPIPS | 0.4909 |
Facial Recognition and Modelling > Face Hallucination | FFHQ 512 x 512 - 16x upscaling | WaveletCNN | http://openaccess.thecvf.com/content_iccv_2017/html/Huang_Wavelet-SRNet_A_Wavelet-Based_ICCV_2017_paper.html | NIQE | 11.450 |
Facial Recognition and Modelling > Face Hallucination | FFHQ 512 x 512 - 16x upscaling | Super-FAN | http://arxiv.org/abs/1712.02765v2 | FID | 63.693 |
Facial Recognition and Modelling > Face Hallucination | FFHQ 512 x 512 - 16x upscaling | Super-FAN | http://arxiv.org/abs/1712.02765v2 | LPIPS | 0.4411 |
Facial Recognition and Modelling > Face Hallucination | FFHQ 512 x 512 - 16x upscaling | Super-FAN | http://arxiv.org/abs/1712.02765v2 | NIQE | 7.444 |
Facial Recognition and Modelling > Face Sketch Synthesis | CUFSF | SCA-GAN | https://arxiv.org/abs/1712.00899v4 | FSIM | 72.9% |
Facial Recognition and Modelling > Face Sketch Synthesis | CUFSF | SCA-GAN | https://arxiv.org/abs/1712.00899v4 | FID | 18.2 |
Facial Recognition and Modelling > Face Sketch Synthesis | CUFSF | SCA-GAN | https://arxiv.org/abs/1712.00899v4 | NLDA | 78 |
Facial Recognition and Modelling > Face Sketch Synthesis | CUFSF | CA-GAN | https://arxiv.org/abs/1712.00899v4 | FSIM | 72.7% |
Facial Recognition and Modelling > Face Sketch Synthesis | CUFSF | CA-GAN | https://arxiv.org/abs/1712.00899v4 | FID | 19.6 |
Facial Recognition and Modelling > Face Sketch Synthesis | CUFSF | CA-GAN | https://arxiv.org/abs/1712.00899v4 | NLDA | 78.1 |
Facial Recognition and Modelling > Face Sketch Synthesis | CUFSF | Residual net + Pseudo Sketch Feature Loss + LSGAN | https://arxiv.org/abs/1812.04929v2 | FSIM | 71.59% |
Facial Recognition and Modelling > Face Sketch Synthesis | CUFSF | Residual net + Pseudo Sketch Feature Loss + LSGAN | https://arxiv.org/abs/1812.04929v2 | SSIM | 40.85% |
Facial Recognition and Modelling > Face Sketch Synthesis | SKSF-A | StyleSketch | https://arxiv.org/abs/2403.11263v1 | LPIPS | 0.1772 |
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