| ViT-tiny |
- Accuracy (8 emotion)
- 58.28
|
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers
2022-07-22 |
연결 없음 |
| Weighted-Loss |
- Accuracy (7 emotion)
- -
- Accuracy (8 emotion)
- 58.0
|
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild
2017-08-14 |
코드 1 |
| ViT-base |
- Accuracy (8 emotion)
- 57.99
|
Emotion Separation and Recognition from a Facial Expression by Generating the Poker Face with Vision Transformers
2022-07-22 |
연결 없음 |
| SL+ SSL in-painting-pl + 20% train (B0) |
- Accuracy (8 emotion)
- 55.36
|
Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation
2021-05-13 |
연결 없음 |
| SL+ SSL puzzling + 20% train (B0) |
- Accuracy (8 emotion)
- 54.98
|
Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation
2021-05-13 |
연결 없음 |
| LResNet50E-IR |
- Accuracy (8 emotion)
- 53.925
|
Exploring Emotion Features and Fusion Strategies for Audio-Video Emotion Recognition
2020-12-27 |
연결 없음 |
| SL + 20% train (B0) |
- Accuracy (8 emotion)
- 52.46
|
Using Self-Supervised Auxiliary Tasks to Improve Fine-Grained Facial Representation
2021-05-13 |
연결 없음 |
| ResEmoteNet |
- Accuracy (7 emotion)
- 72.93
|
ResEmoteNet: Bridging Accuracy and Loss Reduction in Facial Emotion Recognition
2024-09-01 |
코드 1 |
| EmoAffectNet |
- Accuracy (7 emotion)
- 66.49
|
In Search of a Robust Facial Expressions Recognition Model: A Large-Scale Visual Cross-Corpus Study
2022-10-07 |
코드 1 |
| Emotion-GCN |
- Accuracy (7 emotion)
- 66.46
|
Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition
2021-06-07 |
코드 1 |
| FaceBehaviorNet |
- Accuracy (7 emotion)
- 65.40
|
Distribution Matching for Heterogeneous Multi-Task Learning: a Large-scale Face Study
2021-05-08 |
연결 없음 |
| Ada-DF |
- Accuracy (7 emotion)
- 65.34
|
A Dual-Branch Adaptive Distribution Fusion Framework for Real-World Facial Expression Recognition
2023-05-05 |
코드 1 |
| EAC |
- Accuracy (7 emotion)
- 65.32
|
Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition
2022-07-21 |
코드 1 |
| PAENet |
- Accuracy (7 emotion)
- 65.29
|
Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning
2019-06-10 |
코드 1 · 코드 2 |
| DACL |
- Accuracy (7 emotion)
- 65.20
|
Facial Expression Recognition in the Wild via Deep Attentive Center Loss
2021-01-07 |
코드 1 |
| FerNeXt |
- Accuracy (7 emotion)
- 64.77
|
FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel Attention
2023-10-20 |
코드 1 |
| CPG |
- Accuracy (7 emotion)
- 63.57
|
Compacting, Picking and Growing for Unforgetting Continual Learning
2019-10-15 |
코드 1 · 코드 2 |
| Ad-Corre |
- Accuracy (7 emotion)
- 63.36
|
Ad-Corre: Adaptive Correlation-Based Loss for Facial Expression Recognition in the Wild
2022-03-03 |
코드 1 |
| CAKE |
- Accuracy (7 emotion)
- 61.7
|
CAKE: Compact and Accurate K-dimensional representation of Emotion
2018-07-30 |
연결 없음 |
| Facial Motion Prior Network |
- Accuracy (7 emotion)
- 61.52
|
Facial Motion Prior Networks for Facial Expression Recognition
2019-02-23 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |