文章基本信息

标题：Multihead Self Attention Hand Pose Estimation
其他标题：Multihead Self Attention Hand Pose Estimation
本地全文：下载
作者：Zhiqin Zhang ; Bo Zhang ; Fen Li 等
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2020
卷号：218
页码：3023
DOI：10.1051/e3sconf/202021803023
出版社：EDP Sciences
摘要：In This paper, we propose a hand pose estimation neural networks architecture named MSAHP which can improve PCK (percentage correct keypoints) greatly by fusing self-attention module in CNN (Convolutional Neural Networks). The proposed network is based on a ResNet (Residual Neural Network) backbone and concatenate discriminative features through multiple different scale feature maps, then multiple head self-attention module was used to focus on the salient feature map area. In recent years, self-attention mechanism was applicated widely in NLP and speech recognition, which can improve greatly key metrics. But in compute vision especially for hand pose estimation, we did not find the application. Experiments on hand pose estimation dataset demonstrate the improved PCK of our MSAHP than the existing state-of-the-art hand pose estimation methods. Specifically, the proposed method can achieve 93.68% PCK score on our mixed test dataset.