(研究会輪読) Facial Landmark Detection by Deep Multi-task LearningMasahiro Suzuki
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The document summarizes a research paper on facial landmark detection using deep multi-task learning. It proposes a Tasks-Constrained Deep Convolutional Network (TCDCN) that uses facial landmark detection as the main task and related auxiliary tasks like pose estimation and attribute inference to improve performance. The TCDCN learns shared representations across tasks using a deep convolutional network. It introduces task-wise early stopping to halt learning on auxiliary tasks that reach optimal performance early to avoid overfitting and improve convergence on the main task of landmark detection. Experimental results showed the proposed approach outperformed existing methods.