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Hierarchical Graph Convolutional Networks for Action Quality Assessment

Zhou, Kanglei; Ma, Yue; Shum, Hubert P.H.; Liang, Xiaohui

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Kanglei Zhou

Yue Ma

Xiaohui Liang


Action quality assessment (AQA) automatically evaluates how well humans perform actions in a given video, a technique widely used in fields such as rehabilitation medicine, athletic competitions, and specific skills assessment. However, existing works that uniformly divide the video sequence into small clips of equal length suffer from intra-clip confusion and inter-clip incoherence, hindering the further development of AQA. To address this issue, we propose a hierarchical graph convolutional network (GCN). First, semantic information confusion is corrected through clip refinement, generating the ‘shot’ as the basic action unit. We then construct a scene graph by combining several consecutive shots into meaningful scenes to capture local dynamics. These scenes can be viewed as different procedures of a given action, providing valuable assessment cues. The video-level representation is finally extracted via sequential action aggregation among scenes to regress the predicted score distribution, enhancing discriminative features and improving assessment performance. Experiments on the AQA-7, MTLAQA, and JIGSAWS datasets demonstrate the superiority of the proposed hierarchical GCN over state-of-the-art methods.


Zhou, K., Ma, Y., Shum, H. P., & Liang, X. (2023). Hierarchical Graph Convolutional Networks for Action Quality Assessment. IEEE Transactions on Circuits and Systems for Video Technology,

Journal Article Type Article
Acceptance Date May 28, 2023
Online Publication Date May 30, 2023
Publication Date 2023
Deposit Date May 30, 2023
Publicly Available Date May 30, 2023
Journal IEEE Transactions on Circuits and Systems for Video Technology
Print ISSN 1051-8215
Electronic ISSN 1558-2205
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Public URL
Publisher URL


Accepted Journal Article (3.1 Mb)

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