用双路视觉变换器提升人脸关键点检测精度
Cascaded Dual Vision Transformer for Accurate Facial Landmark Detection
- 设计双路视觉变换器，通过通道分裂建模关键点间几何关系
- 在WFLW、COFW、300W上均超越当前最佳方法
- 长跳跃连接保留低层特征，避免信息丢失
人脸关键点检测是计算机视觉中诸多下游任务的基础。本文提出一种基于视觉变换器的新检测器，包含两个独特设计：双路视觉变换器（D-ViT）和长跳跃连接（LSC）。基于特征图通道维度本质上代表热图空间的线性基这一观察，我们提出学习这些线性基之间的关联，以建模关键点间的内在几何关系，由此提出通道分裂视觉变换器（Channel-split ViT）。将该结构整合至标准视觉变换器（即空间分裂视觉变换器），形成双路视觉变换器，作为预测模块的核心。同时，采用长跳跃连接将低层图像特征传递至所有预测模块，防止中间监督导致有用信息丢失。在广泛使用的基准数据集WFLW、COFW、300W上的大量实验表明，所提模型在三个数据集上均优于现有最先进方法。
原文摘要 · Abstract (English)
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.
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