Abstract
In human-computer interaction, head pose estimation profoundly influences application functionality. Although utilizing facial landmarks is valuable for this purpose, existing landmark-based methods prioritize precision over simplicity and model size, limiting their deployment on edge devices and in compute-poor environments. To bridge this gap, we propose \textbf{Grouped Attention Deep Sets (GADS)}, a novel architecture based on the Deep Set framework. By grouping landmarks into regions and employing small Deep Set layers, we reduce computational complexity. Our multihead attention mechanism extracts and combines inter-group information, resulting in a model that is $7.5\times$ smaller and executes $25\times$ faster than the current lightest state-of-the-art model. Notably, our method achieves an impressive reduction, being $4321\times$ smaller than the best-performing model. We introduce vanilla GADS and Hybrid-GADS (landmarks + RGB) and evaluate our models on three benchmark datasets -- AFLW2000, BIWI, and 300W-LP. We envision our architecture as a robust baseline for resource-constrained head pose estimation methods.
Abstract (translated)
在人机交互中，头部姿态估计对应用功能有着深远的影响。尽管使用面部特征点对于这一目的很有价值，但现有的基于特征点的方法更注重精度而非模型的简洁性和大小，这限制了它们在边缘设备和计算资源有限环境中的部署。为解决这一问题，我们提出了一种名为**分组注意力深度集合（Grouped Attention Deep Sets, GADS）**的新架构，该架构基于深度集合框架构建。通过将特征点分组到不同的区域并使用小型的深度集合层，我们减少了计算复杂性。我们的多头注意力机制能够提取和整合跨组的信息，从而创建一个比当前最轻量级的最佳模型小7.5倍且运行速度快25倍的模型。值得注意的是，与表现最佳的模型相比，我们的方法体积缩小了4321倍。我们介绍了基础GADS（vanilla GADS）和混合GADS（landmarks + RGB），并在三个基准数据集——AFLW2000、BIWI以及300W-LP上评估了我们的模型性能。我们认为该架构可以作为资源受限条件下的头部姿态估计方法的坚实基线。
URL
https://arxiv.org/abs/2504.15751
