Contrastive Learning for Action Assessment Using Graph Convolutional Networks With Augmented Virtual Joints

Contrastive Learning for Action Assessment Using Graph Convolutional Networks With Augmented Virtual Joints

A fine-grained detection of posture problems for action assessment has a wide range of applications for health care, sports, and rehabilitation. However, there exist many design challenges, e.g., the difficulty of detecting subtle deviations in actions from standard ones, lack of annotated datasets,...

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Hlavní autoři: Chung-In Joung, Seunghwan Byun, Seungjun Baek
Médium: Článek
Jazyk:English
Vydáno: IEEE 2023-01-01
Edice:IEEE Access
Témata:
Graph convolutional networks
augmented virtual joints
contrastive learning
pooling
fine-grained action classification
On-line přístup:https://ieeexplore.ieee.org/document/10217805/

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https://ieeexplore.ieee.org/document/10217805/

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