Adaptive Attention Memory Graph Convolutional Networks for Skeleton-Based Action Recognition

Adaptive Attention Memory Graph Convolutional Networks for Skeleton-Based Action Recognition

Graph Convolutional Networks (GCNs) have attracted a lot of attention and shown remarkable performance for action recognition in recent years. For improving the recognition accuracy, how to build graph structure adaptively, select key frames and extract discriminative features are the key problems o...

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Bibliographic Details
Main Authors: Di Liu, Hui Xu, Jianzhong Wang, Yinghua Lu, Jun Kong, Miao Qi
Format: Article
Language:English
Published: MDPI AG 2021-10-01
Series:Sensors
Subjects:
graph convolutional networks
action recognition
attention
skeleton sequence
Online Access:https://www.mdpi.com/1424-8220/21/20/6761

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