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Jul, 2020
基于 Poincaré 几何学的混合空间维度用于 3D 骨架动作识别
Mix Dimension in Poincaré Geometry for 3D Skeleton-based Action Recognition
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Wei Peng, Jingang Shi, Zhaoqiang Xia, Guoying Zhao
TL;DR
本文提出了在Riemann流形上构建的空间-时间图卷积神经网络(ST-GCN)架构,以更好地对结构数据进行建模,提高动作识别准确性,并在两个最大规模的3D数据集上进行了评估,证明了该方法的有效性和优越性。
Abstract
graph convolutional networks
(GCNs) have already demonstrated their powerful ability to model the irregular data, e.g., skeletal data in human
action recognition
, providing an exciting new way to fuse rich struct
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