Advanced Manufacturing Technology Center, Shandong University of Science and Technology, Qingdao, Shandong province, China.
PLoS One. 2018 Dec 12;13(12):e0208497. doi: 10.1371/journal.pone.0208497. eCollection 2018.
Attention maps have been fused in the VggNet structure (EAC-Net) [1] and have shown significant improvement compared to that of the VggNet structure. However, in [1], E-Net was designed based on the facial action unit (AU) center and for facial AU detection only. Thus, for the use of attention maps in every image type, this paper proposed a new convolutional neural network (CNN) structure, P_VggNet, comprising the following parts: P_Net and VggNet with 16 layers (VggNet-16). The generation approach of P_Net was designed, and the P_VggNet structure was proposed. To prove the efficiency of P_VggNet, we designed two experiments, which indicated that P_VggNet could more efficiently extract image features than VggNet-16.
注意力图已经融合到 VggNet 结构中(EAC-Net)[1],与 VggNet 结构相比,表现出了显著的改进。然而,在[1]中,E-Net 是基于面部动作单元(AU)中心设计的,仅用于面部 AU 检测。因此,为了在每种图像类型中使用注意力图,本文提出了一种新的卷积神经网络(CNN)结构 P_VggNet,它由以下部分组成:P_Net 和具有 16 层的 VggNet(VggNet-16)。设计了 P_Net 的生成方法,并提出了 P_VggNet 结构。为了证明 P_VggNet 的效率,我们设计了两个实验,结果表明 P_VggNet 比 VggNet-16 更有效地提取图像特征。