Abstract
Facial landmark detection is a fundamental problem in computer vision for many downstream applications. This paper introduces a new facial landmark detector based on vision transformers, which consists of two unique designs: Dual Vision Transformer (D-ViT) and Long Skip Connections (LSC). Based on the observation that the channel dimension of feature maps essentially represents the linear bases of the heatmap space, we propose learning the interconnections between these linear bases to model the inherent geometric relations among landmarks via Channel-split ViT. We integrate such channel-split ViT into the standard vision transformer (i.e., spatial-split ViT), forming our Dual Vision Transformer to constitute the prediction blocks. We also suggest using long skip connections to deliver low-level image features to all prediction blocks, thereby preventing useful information from being discarded by intermediate supervision. Extensive experiments are conducted to evaluate the performance of our proposal on the widely used benchmarks, i.e., WFLW, COFW, and 300W, demonstrating that our model outperforms the previous SOTAs across all three benchmarks.
Abstract (translated)
面部特征点检测是计算机视觉中许多下游应用的基础问题。本文介绍了一种基于视觉变换器的新面部特征点检测器，该检测器包含两个独特的设计：双视觉变换器（D-ViT）和长跳跃连接（LSC）。根据特征图的通道维度实际上表示热图空间线性基底这一观察结果，我们提出了通过通道分割ViT学习这些线性基底之间的相互联系，以建模地标间的固有几何关系。我们将这种通道分割ViT整合到标准视觉变换器（即空间分割ViT）中，形成我们的双视觉变换器，构成预测块。此外，我们还建议使用长跳跃连接将低级图像特征传递给所有预测块，从而防止因中间监督而丢失有用信息。我们在广泛使用的基准测试集上进行了广泛的实验，包括WFLW、COFW和300W，结果表明我们的模型在这三个基准测试集中都优于之前的最佳技术水平。
URL
https://arxiv.org/abs/2411.07167
