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 Expression Recognition (FER) | FER+ | Local Learning Deep + BOW | https://arxiv.org/abs/1804.10892v7 | Accuracy | 87.76 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER+ | Ensemble with Shared Representations (ESR-9) | https://arxiv.org/abs/2001.06338v1 | Accuracy | 87.15 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Aff-Wild2 | GReFEL | https://arxiv.org/abs/2410.15927v1 | Accuracy | 72.48 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Aff-Wild2 | EmoAffectNet LSTM | https://www.sciencedirect.com/science/article/abs/pii/S0925231222012656 | UAR | 52.9 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FERG | DeepEmotion | http://arxiv.org/abs/1902.01019v1 | Accuracy | 99.3 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FERG | GReFEL | https://arxiv.org/abs/2410.15927v1 | Accuracy | 98.18 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | BP4D | Norface | https://arxiv.org/abs/2407.15617v1 | ICC | 0.74 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | BP4D | Ours (VGG-F) | https://arxiv.org/abs/2103.16554v2 | ICC | 0.719 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | Cohn-Kanade | Sequential forward selection | http://arxiv.org/abs/1701.01879v2 | Accuracy | 88.7% |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | JAFFE | TL | https://www.mdpi.com/2079-9292/10/9/1036 | Accuracy | 99.52 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | JAFFE | GReFEL | https://arxiv.org/abs/2410.15927v1 | Accuracy | 96.67 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | JAFFE | ViT | https://arxiv.org/abs/2107.03107v4 | Accuracy | 94.83 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | JAFFE | DeepEmotion | http://arxiv.org/abs/1902.01019v1 | Accuracy | 92.8 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | EfficientFER | https://ieeexplore.ieee.org/document/11017006 | Accuracy | 82.47 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | FERNeXt-SDAFE | https://ieeexplore.ieee.org/abstract/document/10888031 | Accuracy | 81.33 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | ResEmoteNet | https://arxiv.org/abs/2409.10545v2 | Accuracy | 79.79 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | Ensemble ResMaskingNet with 6 other CNNs | https://ieeexplore.ieee.org/document/9411919 | Accuracy | 76.82 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | Mini-ResEmoteNet (A) | https://arxiv.org/abs/2501.18538v1 | Accuracy | 76.33 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | EmoNeXt | https://arxiv.org/abs/2501.08199v1 | Accuracy | 76.12 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | Segmentation VGG-19 | https://link.springer.com/article/10.1007/s41870-023-01184-z | Accuracy | 75.97 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | Local Learning Deep+BOW | https://arxiv.org/abs/1804.10892v7 | Accuracy | 75.42 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | LHC-Net | https://arxiv.org/abs/2111.07224v2 | Accuracy | 74.42 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | Residual Masking Network | https://ieeexplore.ieee.org/document/9411919 | Accuracy | 74.14 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | ResNet18 With Tricks | https://github.com/LetheSec/Fer2013-Recognition-Pytorch/blob/main/README.md | Accuracy | 73.70 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | VGGNet | https://arxiv.org/abs/2105.03588v1 | Accuracy | 73.28 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | CNN Hyperparameter Optimisation | https://www.semanticscholar.org/paper/Convolutional-Neural-Network-Hyperparameters-for-Vulpe-Grigora%C5%9Fi-Grigore/fe344427eafecc60a1ba29beb87a46e91b7c1420#related-papers | Accuracy | 72.16 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | Ad-Corre | https://ieeexplore.ieee.org/document/9727163 | Accuracy | 72.03 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | Mini-ResEmoteNet (B) | https://arxiv.org/abs/2501.18538v1 | Accuracy | 70.20 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | DeepEmotion | http://arxiv.org/abs/1902.01019v1 | Accuracy | 70.02 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | FER2013 | Local Learning BOW | http://arxiv.org/abs/1307.0414v1 | Accuracy | 67.48 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | ResEmoteNet | https://arxiv.org/abs/2409.10545v2 | Overall Accuracy | 94.76 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | FMAE | https://arxiv.org/abs/2407.11243v2 | Overall Accuracy | 93.45 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | QCS | https://arxiv.org/abs/2411.01988v5 | Overall Accuracy | 93.02 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | Norface | https://arxiv.org/abs/2407.15617v1 | Overall Accuracy | 92.97 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | S2D | https://arxiv.org/abs/2312.05447v2 | Overall Accuracy | 92.57 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | BTN | https://arxiv.org/abs/2407.04218v1 | Overall Accuracy | 92.54 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | BTN | https://arxiv.org/abs/2407.04218v1 | Avg. Accuracy | 87.3 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | GReFEL | https://arxiv.org/abs/2410.15927v1 | Overall Accuracy | 92.47 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | DDAMFN++ | https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=P4efBMcAAAAJ&citation_for_view=P4efBMcAAAAJ:d1gkVwhDpl0C | Overall Accuracy | 92.34 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | DCJT | https://ieeexplore.ieee.org/document/10483295 | Overall Accuracy | 92.24 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | POSTER++ | https://arxiv.org/abs/2301.12149v2 | Overall Accuracy | 92.21 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | APViT | https://arxiv.org/abs/2212.05463v1 | Overall Accuracy | 91.98 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | DDAMFN | https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=P4efBMcAAAAJ&citation_for_view=P4efBMcAAAAJ:d1gkVwhDpl0C | Overall Accuracy | 91.35 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | ViT-base + MAE | https://arxiv.org/abs/2207.11081v4 | Overall Accuracy | 91.07 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | LFNSB | https://www.preprints.org/manuscript/202408.1304/v1 | Overall Accuracy | 91.07 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | ExpLLM | https://arxiv.org/abs/2409.02828v1 | Overall Accuracy | 91.03 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | EAC(ResNet-50) | https://arxiv.org/abs/2207.10299v2 | Overall Accuracy | 90.35 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | Ada-DF | https://ieeexplore.ieee.org/document/10097033 | Overall Accuracy | 90.04 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | DAN | https://arxiv.org/abs/2109.07270v6 | Overall Accuracy | 89.70 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | RUL (ResNet-18) | http://proceedings.neurips.cc/paper/2021/hash/9332c513ef44b682e9347822c2e457ac-Abstract.html | Overall Accuracy | 88.98 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | PSR | https://doi.org/10.1109/ACCESS.2020.3010018 | Overall Accuracy | 88.98 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | FerNeXt | https://ieeexplore.ieee.org/document/10278345 | Overall Accuracy | 88.56 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | EfficientFace | https://ojs.aaai.org/index.php/AAAI/article/view/16465 | Overall Accuracy | 88.36 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | MA-Net | https://ieeexplore.ieee.org/document/9474949 | Overall Accuracy | 88.36 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | DACL (ResNet-18) | https://openaccess.thecvf.com/content/WACV2021/html/Farzaneh_Facial_Expression_Recognition_in_the_Wild_via_Deep_Attentive_Center_WACV_2021_paper.html | Overall Accuracy | 87.78 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | DACL (ResNet-18) | https://openaccess.thecvf.com/content/WACV2021/html/Farzaneh_Facial_Expression_Recognition_in_the_Wild_via_Deep_Attentive_Center_WACV_2021_paper.html | Avg. Accuracy | 80.44 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | MixAugment | https://arxiv.org/abs/2205.04442v1 | Overall Accuracy | 87.54 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | MixAugment | https://arxiv.org/abs/2205.04442v1 | Avg. Accuracy | 77.30 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | ViT-base | https://arxiv.org/abs/2207.11081v4 | Overall Accuracy | 87.22 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | ViT-tiny | https://arxiv.org/abs/2207.11081v4 | Overall Accuracy | 87.03 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | Ad-Corre | https://ieeexplore.ieee.org/document/9727163 | Overall Accuracy | 86.96 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | RAN (ResNet-18) | https://arxiv.org/abs/1905.04075v2 | Overall Accuracy | 86.9 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | C-EXPR-NET | http://openaccess.thecvf.com//content/CVPR2023/html/Kollias_Multi-Label_Compound_Expression_Recognition_C-EXPR_Database__Network_CVPR_2023_paper.html | Avg. Accuracy | 87.5 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | C MT PSR | https://arxiv.org/abs/2401.01219v2 | Avg. Accuracy | 84.8 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | C MT VGGFACE | https://arxiv.org/abs/2401.01219v2 | Avg. Accuracy | 81.4 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | FaceBehaviorNet | https://arxiv.org/abs/2105.03790v1 | Avg. Accuracy | 78 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | VGG-FACE | https://arxiv.org/abs/1811.05027v2 | Avg. Accuracy | 77.5 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | RAF-DB | MT-ArcVGG | https://arxiv.org/abs/1910.04855v1 | Avg. Accuracy | 76 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | DISFA | Norface | https://arxiv.org/abs/2407.15617v1 | ICC | 0.67 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | DISFA | Ours (VGG-F) | https://arxiv.org/abs/2103.16554v2 | ICC | 0.598 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | SFEW | Ada-DF | https://ieeexplore.ieee.org/document/10097033 | Accuracy | 60.46 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | SFEW | RAN (VGG16+ResNet18) | https://arxiv.org/abs/1905.04075v2 | Accuracy | 56.4 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | SFEW | ViT + SE | https://arxiv.org/abs/2107.03107v4 | Accuracy | 54.29 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | SFEW | Island Loss | http://arxiv.org/abs/1710.03144v3 | Accuracy | 52.52 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | Norface | https://arxiv.org/abs/2407.15617v1 | Accuracy (8 emotion) | 68.69 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | DDAMFN++ | https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=P4efBMcAAAAJ&citation_for_view=P4efBMcAAAAJ:d1gkVwhDpl0C | Accuracy (7 emotion) | 67.36 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | DDAMFN++ | https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=P4efBMcAAAAJ&citation_for_view=P4efBMcAAAAJ:d1gkVwhDpl0C | Accuracy (8 emotion) | 65.04 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | FMAE | https://arxiv.org/abs/2407.11243v2 | Accuracy (8 emotion) | 64.79 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | QCS | https://arxiv.org/abs/2411.01988v5 | Accuracy (7 emotion) | 67.94 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | QCS | https://arxiv.org/abs/2411.01988v5 | Accuracy (8 emotion) | 64.4 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | BTN | https://arxiv.org/abs/2407.04218v1 | Accuracy (7 emotion) | 67.60 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | BTN | https://arxiv.org/abs/2407.04218v1 | Accuracy (8 emotion) | 64.29 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | DDAMFN | https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=P4efBMcAAAAJ&citation_for_view=P4efBMcAAAAJ:d1gkVwhDpl0C | Accuracy (7 emotion) | 67.03 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | DDAMFN | https://scholar.google.com/citations?view_op=view_citation&hl=zh-CN&user=P4efBMcAAAAJ&citation_for_view=P4efBMcAAAAJ:d1gkVwhDpl0C | Accuracy (8 emotion) | 64.25 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | EmoNeXt | https://link.springer.com/article/10.1007/s00521-024-10938-0 | Accuracy (7 emotion) | 67.46 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | EmoNeXt | https://link.springer.com/article/10.1007/s00521-024-10938-0 | Accuracy (8 emotion) | 64.13 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | POSTER++ | https://arxiv.org/abs/2301.12149v2 | Accuracy (7 emotion) | 67.49 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | POSTER++ | https://arxiv.org/abs/2301.12149v2 | Accuracy (8 emotion) | 63.77 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | LFNSB | https://www.preprints.org/manuscript/202408.1304/v1 | Accuracy (7 emotion) | 66.57 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | LFNSB | https://www.preprints.org/manuscript/202408.1304/v1 | Accuracy (8 emotion) | 63.12 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | S2D | https://arxiv.org/abs/2312.05447v2 | Accuracy (7 emotion) | 67.62 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | S2D | https://arxiv.org/abs/2312.05447v2 | Accuracy (8 emotion) | 63.06 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | Multi-task EfficientNet-B2 | https://ieeexplore.ieee.org/document/9815154 | Accuracy (7 emotion) | 66.29 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | Multi-task EfficientNet-B2 | https://ieeexplore.ieee.org/document/9815154 | Accuracy (8 emotion) | 63.03 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | MT-ArcRes | https://arxiv.org/abs/1910.04855v1 | Accuracy (8 emotion) | 63 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | ExpLLM | https://arxiv.org/abs/2409.02828v1 | Accuracy (7 emotion) | 65.93 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | ExpLLM | https://arxiv.org/abs/2409.02828v1 | Accuracy (8 emotion) | 62.86 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | Vit-base + MAE | https://arxiv.org/abs/2207.11081v4 | Accuracy (8 emotion) | 62.42 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | CAGE | https://arxiv.org/abs/2404.14975v1 | Accuracy (7 emotion) | 66.6 |
Facial Recognition and Modelling > Facial Expression Recognition (FER) | AffectNet | CAGE | https://arxiv.org/abs/2404.14975v1 | Accuracy (8 emotion) | 62.2 |
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