补充材料

Chia-Wen Kuo, Chih-Yao Ma, Jia-Bin Huang, Z. Kira
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引用次数: 1

摘要

表1、2和3充分展示了拟议的ADNet架构。关于我们实验设置的详细介绍,请参考我们稿件的第4节。表中P∗、H∗点和H∗边分别表示光滑ADL1损耗、AWing损耗和AWing损耗的输入。Npoint和Nedge表示点和边的数量,根据每个数据集的不同而不同。堆叠4hg时,HG (Hour Glass)的损失率分别为1/8、1/4、1/2和1。第四个头分支输出P3是每个地标的最终预测坐标,由软argmax运算得到。在表2中,E2P Transform的目标是考虑邻接关系为,将Ĥedge(边缘通道)转换为Hedge (Npoint通道)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Supplementary Materials
Tables 1, 2 and 3 fully demonstrate the architecture of the proposed ADNet. For detailed introduction of our experimental setting, please refer to Section 4 of our manuscript. In the table, P∗, H∗point and H∗edge denote the inputs of smooth ADL1 loss, AWing loss and AWing loss, respectively. Npoint and Nedge indicate the number of points and edges, which varies according to each dataset. The loss weights of Hour Glass (HG) for stacked 4 HGs are respectively 1/8, 1/4, 1/2, and 1. The fourth head branch outputs P3 is the final predicted coordinate of each landmark, which is derived from the soft argmax operation. In Table 2, the goal of E2P Transform is to convert Ĥedge (Nedge channels) into Hedge (Npoint channels) by considering the adjacency relationship as
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