University of Oulu

A. Mostafa, W. Peng and G. Zhao, "Hyperbolic Spatial Temporal Graph Convolutional Networks," 2022 IEEE International Conference on Image Processing (ICIP), Bordeaux, France, 2022, pp. 3301-3305, doi: 10.1109/ICIP46576.2022.9897522

Hyperbolic spatial temporal graph convolutional networks

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Author: Mostafa, Abdelrahman1; Peng, Wei1; Zhao, Guoying1
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 4.1 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2023-03-31


Spatial-temporal graph convolutional networks (ST-GCNs) have been successfully applied for dynamic graphs representation learning, such as modeling skeleton-based human actions. However, ST-GCNs embed these non-Euclidean graph structures into Euclidean space, which is not the natural space to represent such structures as embedding them in this space incurs a large distortion. In this work, we make use of hyperbolic non-Euclidean geometry and construct compact ST-GCNs in the hyperbolic space. It can be shown that hyperbolic ST-GCNs (HST-GCNs) outperform the corresponding Euclidean counterparts. Additionally, these compact hyperbolic models can be used to increase the performance of large complex Euclidean models. Moreover, we show that the same or even better performance of large Euclidean models can be achieved by fusing the scores of smaller Euclidean models and a compact hyperbolic model. This in turn leads to reducing the total number of model parameters and hence model size. To validate the performance of these hyperbolic networks, we conducted extensive experiments on NTU RGB+D, NTU RGB+D 120 and Kinectics-Skeleton datasets for human action recognition.

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Series: IEEE International Conference on Image Processing
ISSN: 1522-4880
ISSN-E: 2381-8549
ISSN-L: 1522-4880
ISBN: 978-1-6654-9620-9
ISBN Print: 978-1-6654-9621-6
Pages: 3301 - 3305
DOI: 10.1109/ICIP46576.2022.9897522
Host publication: 2022 IEEE International Conference on Image Processing (ICIP)
Conference: IEEE International Conference on Image Processing
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by the Academy of Finland for Academy Professor project EmotionAI (grants 336116, 345122) and ICT 2023 project (grant 328115), as well as the CSC-IT Center for Science, Finland, for computational resources.
Academy of Finland Grant Number: 336116
Detailed Information: 336116 (Academy of Finland Funding decision)
345122 (Academy of Finland Funding decision)
328115 (Academy of Finland Funding decision)
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