A Deep Regression Architecture With Two-Stage Re-Initialization for High Performance Facial Landmark Detection

Regression based facial landmark detection methods usually learns a series of regression functions to update the landmark positions from an initial estimation. Most of existing approaches focus on learning effective mapping functions with robust image features to improve performance... (read more)

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Methods used in the Paper


METHOD TYPE
Spatial Transformer
Image Model Blocks
Residual Connection
Skip Connections
BPE
Subword Segmentation
Dense Connections
Feedforward Networks
Label Smoothing
Regularization
ReLU
Activation Functions
Adam
Stochastic Optimization
Softmax
Output Functions
Dropout
Regularization
Multi-Head Attention
Attention Modules
Layer Normalization
Normalization
Scaled Dot-Product Attention
Attention Mechanisms
Transformer
Transformers