Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition

Wei Wei, Tom De Schepper, Kevin Mets

2024

Abstract

Continual learning (CL) is the research field that aims to build machine learning models that can accumulate knowledge continuously over different tasks without retraining from scratch. Previous studies have shown that pre-training graph neural networks (GNN) may lead to negative transfer (Hu et al., 2020) after fine-tuning, a setting which is closely related to CL. Thus, we focus on studying GNN in the continual graph learning (CGL) setting. We propose the first continual graph learning benchmark for spatio-temporal graphs and use it to benchmark well-known CGL methods in this novel setting. The benchmark is based on the N-UCLA and NTU-RGB+D datasets for skeleton-based action recognition. Beyond benchmarking for standard performance metrics, we study the class and task-order sensitivity of CGL methods, i.e., the impact of learning order on each class/task’s performance, and the architectural sensitivity of CGL methods with backbone GNN at various widths and depths. We reveal that task-order robust methods can still be class-order sensitive and observe results that contradict previous empirical observations on architectural sensitivity in CL.

Paper Citation

in Harvard Style

Wei W., De Schepper T. and Mets K. (2024). Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP; ISBN 978-989-758-679-8, SciTePress, pages 639-651. DOI: 10.5220/0012394400003660

in Bibtex Style

@conference{visapp24,
author={Wei Wei and Tom De Schepper and Kevin Mets},
title={Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP},
year={2024},
pages={639-651},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012394400003660},
isbn={978-989-758-679-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: VISAPP
TI - Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition
SN - 978-989-758-679-8
AU - Wei W.
AU - De Schepper T.
AU - Mets K.
PY - 2024
SP - 639
EP - 651
DO - 10.5220/0012394400003660
PB - SciTePress