Facial landmark detection in the wild remains a challenging problem in computer vision. Deep learning-based methods currently play a leading role in solving this. However, these approaches generally focus on local feature learning and ignore global relationships. Therefore, in this study, a self-attention mechanism is introduced into facial landmark detection. Specifically, a coarse-to-fine facial landmark detection method is proposed that uses two stacked hourglasses as the backbone, with a new landmark-guided self-attention (LGSA) block inserted between them. The LGSA block learns the global relationships between different positions on the feature map and allows feature learning to focus on the locations of landmarks with the help of a landmark-specific attention map, which is generated in the first-stage hourglass model. A novel attentional consistency loss is also proposed to ensure the generation of an accurate landmark-specific attention map. A new channel transformation block is used as the building block of the hourglass model to improve the model's capacity. The coarse-to-fine strategy is adopted during and between phases to reduce complexity. Extensive experimental results on public datasets demonstrate the superiority of our proposed method against state-of-the-art models.

中文翻译：

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基于自注意力机制的粗到细人脸地标检测方法

在野外进行面部界标检测仍然是计算机视觉中的一个挑战性问题。当前，基于深度学习的方法在解决这一问题方面起着主导作用。但是，这些方法通常专注于局部特征学习，而忽略全局关系。因此，在这项研究中，自我注意机制被引入到面部标志检测中。具体而言，提出了一种从粗糙到精细的面部界标检测方法，该方法使用两个堆叠的沙漏作为主干，并在它们之间插入新的界标引导的自我注意（LGSA）块。LGSA块可学习特征图上不同位置之间的全局关系，并允许特征学习借助在第一阶段沙漏模型中生成的特定于地标的注意图来关注地标的位置。还提出了一种新颖的注意力一致性损失，以确保生成准确的特定于地标的注意力图。新的通道转换块用作沙漏模型的构建块，以提高模型的容量。在阶段之间以及阶段之间采用从粗到精的策略以降低复杂性。在公共数据集上的大量实验结果证明了我们提出的方法相对于最新模型的优越性。