论文标题
计算有效的深神经网络，具有差异的注意力图，用于面部动作单元检测
Computational efficient deep neural network with difference attention maps for facial action unit detection
论文作者
论文摘要
在本文中，我们提出了基于差异图像的计算有效端到端训练深神经网络（CEDNN）模型和空间注意图。首先，差异图像是通过图像处理生成的。然后使用不同的阈值获得五个差异图像的二进制图像，这些阈值用作空间注意图。我们使用小组卷积来降低模型的复杂性。跳过连接和$ \ text {1} \ times \ text {1} $卷积也用于确保良好的性能，即使网络模型不深。作为输入，可以选择性地将空间注意图送入每个块的输入中。该功能地图倾向于将其重点放在与目标任务相关的零件上。此外，我们只需要调整分类器的参数即可训练不同数量的AU。它可以轻松地扩展到不同的数据集，而不会增加过多的计算。大量实验结果表明，所提出的CEDNN显然比DISFA+和CK+数据集的传统深度学习方法更好。添加空间注意图后，结果比最先进的AU检测方法更好。同时，网络的比例很小，运行速度很快，并且对实验设备的要求很低。
In this paper, we propose a computational efficient end-to-end training deep neural network (CEDNN) model and spatial attention maps based on difference images. Firstly, the difference image is generated by image processing. Then five binary images of difference images are obtained using different thresholds, which are used as spatial attention maps. We use group convolution to reduce model complexity. Skip connection and $\text{1}\times \text{1}$ convolution are used to ensure good performance even if the network model is not deep. As an input, spatial attention map can be selectively fed into the input of each block. The feature maps tend to focus on the parts that are related to the target task better. In addition, we only need to adjust the parameters of classifier to train different numbers of AU. It can be easily extended to varying datasets without increasing too much computation. A large number of experimental results show that the proposed CEDNN is obviously better than the traditional deep learning method on DISFA+ and CK+ datasets. After adding spatial attention maps, the result is better than the most advanced AU detection method. At the same time, the scale of the network is small, the running speed is fast, and the requirement for experimental equipment is low.
