论文标题
稀疏的本地补丁变压器，用于健壮的面部对齐和地标固有的关系学习
Sparse Local Patch Transformer for Robust Face Alignment and Landmarks Inherent Relation Learning
论文作者
论文摘要
近年来，热图回归方法占据了面部对齐区域，而它们忽略了不同地标之间的固有关系。在本文中，我们提出了一个稀疏的本地斑块变压器（SLPT），用于学习固有的关系。 SLPT从局部贴片中生成每个单个地标的表示，并通过基于注意机制的自适应固有关系来汇总它们。每个地标的子像素坐标是根据汇总特征独立预测的。此外，进一步引入了一个与SLPT合并的粗到精细框架，这使得初始地标可以使用动态调整大小的本地贴片的细粒度逐渐收敛到目标面部标志。在包括WFLW，300W和COFW在内的三个流行基准上进行的广泛实验表明，通过学习面部地标之间的固有关系，该提出的方法在最先进的水平上起作用，计算复杂性较小。该代码可在项目网站上找到。
Heatmap regression methods have dominated face alignment area in recent years while they ignore the inherent relation between different landmarks. In this paper, we propose a Sparse Local Patch Transformer (SLPT) for learning the inherent relation. The SLPT generates the representation of each single landmark from a local patch and aggregates them by an adaptive inherent relation based on the attention mechanism. The subpixel coordinate of each landmark is predicted independently based on the aggregated feature. Moreover, a coarse-to-fine framework is further introduced to incorporate with the SLPT, which enables the initial landmarks to gradually converge to the target facial landmarks using fine-grained features from dynamically resized local patches. Extensive experiments carried out on three popular benchmarks, including WFLW, 300W and COFW, demonstrate that the proposed method works at the state-of-the-art level with much less computational complexity by learning the inherent relation between facial landmarks. The code is available at the project website.
