梳理眼球方向回归的深度学习方法，发现新模型实际表现不如旧模型
A Survey on Deep Learning-based Gaze Direction Regression: Searching for the State-of-the-art
- 系统分析输入数据、模型结构与损失函数设计
- 统一验证设置后发现最新方法反而不如老方法
- 时序模型在静态测试中优于静态模型
本文综述了基于深度学习的眼球方向向量回归方法，详细描述了多种公开方法的输入数据、模型架构和损失函数。同时整理了可用于训练和评估的常用数据集。我们注意到文献中的结果常因验证/测试集不同而难以比较。为此，我们在同一验证设置下重新评估了多个方法在常见野外数据集Gaze360上的表现。实验表明，尽管最新方法宣称达到最先进水平，但实际性能显著低于一些较早的方法。最后，我们发现时序模型在静态测试条件下优于静态模型。
原文摘要 · Abstract (English)
In this paper, we present a survey of deep learning-based methods for the regression of gaze direction vector from head and eye images. We describe in detail numerous published methods with a focus on the input data, architecture of the model, and loss function used to supervise the model. Additionally, we present a list of datasets that can be used to train and evaluate gaze direction regression methods. Furthermore, we noticed that the results reported in the literature are often not comparable one to another due to differences in the validation or even test subsets used. To address this problem, we re-evaluated several methods on the commonly used in-the-wild Gaze360 dataset using the same validation setup. The experimental results show that the latest methods, although claiming state-of-the-art results, significantly underperform compared with some older methods. Finally, we show that the temporal models outperform the static models under static test conditions.
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