Research map / Detection and segmentation / CNN features
Exploiting Semantic Embedding and Visual Feature for Facial Action Unit Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add AU semantic descriptions encoded with Intra-AU and Inter-AU attention, then use them to guide visual attention | SEV-Net with ResNet-18 · BP4D | F1-score (Avg): 62.9 -> 63.9 | Helped |
| Add AU semantic descriptions encoded with Intra-AU and Inter-AU attention, then use them to guide visual attention | SEV-Net with ResNet-18 · DISFA | F1-score (Avg): 56.9 -> 58.8 | Helped |
| Add AU semantic descriptions encoded with Intra-AU and Inter-AU attention, then use them to guide visual attention | SEV-Net with ResNet-18 · BP4D+ | F1-score (Avg): 60.5 -> 61.5 | Helped |
Closest papers by meaning
- Semantic Relationships Guided Representation Learning for Facial Action Unit Recognition (AAAI 2019 · similarity 0.91)
- Facial Action Unit Detection With Transformers (CVPR 2021 · similarity 0.90)
- Facial Action Unit Intensity Estimation via Semantic Correspondence Learning with Dynamic Graph Convolution (AAAI 2020 · similarity 0.89)
- Knowledge-Driven Self-Supervised Representation Learning for Facial Action Unit Recognition (CVPR 2022 · similarity 0.89)
- Local Relationship Learning With Person-Specific Shape Regularization for Facial Action Unit Detection (CVPR 2019 · similarity 0.88)
- AUEditNet: Dual-Branch Facial Action Unit Intensity Manipulation with Implicit Disentanglement (CVPR 2024 · similarity 0.86)
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