论文标题
用于原始眼睛跟踪数据分割，生成和重建的完全卷积神经网络
Fully Convolutional Neural Networks for Raw Eye Tracking Data Segmentation, Generation, and Reconstruction
论文作者
论文摘要
在本文中，我们使用完全卷积的神经网络进行眼镜跟踪数据的语义分割。我们还将这些网络用于重建，并与各种自动编码器一起生成眼动数据。我们方法的第一个改进是，由于使用完全卷积网络，因此无需输入窗口，因此可以直接处理任何输入大小。第二个改进是，使用和生成的数据是原始的眼睛跟踪数据（位置x，y和时间），而无需预处理。这是通过在第一层预先定位过滤器和沿Z轴构建输入张量来实现的。我们评估了三个公开可用数据集的方法，并将结果与最新的状态进行了比较。
In this paper, we use fully convolutional neural networks for the semantic segmentation of eye tracking data. We also use these networks for reconstruction, and in conjunction with a variational auto-encoder to generate eye movement data. The first improvement of our approach is that no input window is necessary, due to the use of fully convolutional networks and therefore any input size can be processed directly. The second improvement is that the used and generated data is raw eye tracking data (position X, Y and time) without preprocessing. This is achieved by pre-initializing the filters in the first layer and by building the input tensor along the z axis. We evaluated our approach on three publicly available datasets and compare the results to the state of the art.
