MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction-Reference-Cited by-同舟云学术

MSR-GCN: Multi-Scale Residual Graph Convolution Networks for Human Motion Prediction

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
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Author:

Dang Lingwei¹,Nie Yongwei¹,Long Chengjiang²,Zhang Qing³,Li Guiqing¹

Affiliation:

1. South China University of Technology,School of Computer Science and Engineering,China

2. JD Finance America Corporation,USA

3. Sun Yat-sen University,School of Computer Science and Engineering,China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Guangdong Province

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09711152.pdf?arnumber=9711152

Reference53 articles.

1. Skeleton-Based Action Recognition With Directed Graph Neural Networks

2. Sgcn: Sparse graph convolution for pedestrian trajectory prediction;shi;Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition,2021

3. Learning Trajectory Dependencies for Human Motion Prediction

4. A Joint Gaussian Process Model for Active Visual Recognition with Expertise Estimation in Crowdsourcing

5. Correlational Gaussian Processes for Cross-Domain Visual Recognition

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