Research map / Architectures and efficiency / RNNs and normalization
Mnemonic Descent Method: A Recurrent Process Applied for End-To-End Face Alignment
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace hand-crafted SIFT features with learned convolutional features (ablation) | MDM · 300W full set | point-to-point error | Helped |
| Increase the number of recurrent time-steps (ablation) | MDM · 300W full set | mean interocular error | Mixed |
| Use more than four recurrent time-steps (ablation) | MDM · 300W full set | mean interocular error | No clear effect |
| Use MDM rather than CFSS | face alignment · 300W competition test set, 51 points | AUC (%): 50.79 -> 56.34 | Helped |
| Use MDM rather than CFSS | face alignment · 300W competition test set, 68 points | AUC (%): 39.81 -> 45.32 | Helped |
Closest papers by meaning
- Self-Reinforced Cascaded Regression for Face Alignment (AAAI 2018 · similarity 0.90)
- Unconstrained Face Alignment via Cascaded Compositional Learning (CVPR 2016 · similarity 0.89)
- Direct Shape Regression Networks for End-to-End Face Alignment (CVPR 2018 · similarity 0.89)
- Facial Landmarks Detection by Self-Iterative Regression Based Landmarks-Attention Network (AAAI 2018 · similarity 0.88)
- CLKN: Cascaded Lucas-Kanade Networks for Image Alignment (CVPR 2017 · similarity 0.87)
- Large-Pose Face Alignment via CNN-Based Dense 3D Model Fitting (CVPR 2016 · similarity 0.87)
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