eprintid: 10189813
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/18/98/13
datestamp: 2024-03-27 16:20:51
lastmod: 2024-03-27 16:20:51
status_changed: 2024-03-27 16:20:51
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Myung, Woomin
creators_name: Su, Nan
creators_name: Xue, Jing-Hao
creators_name: Wang, Guijin
title: DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action Recognition
ispublished: inpress
divisions: UCL
divisions: B04
divisions: C06
divisions: F61
keywords: Convolution
,
Adaptation models
,
Topology
,
Deformable models
,
Correlation
,
Convolutional neural networks
,
Laplace equations
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Graph convolutional networks (GCN) have recently been studied to exploit the graph topology of the human body for skeleton-based action recognition. However, most of these methods unfortunately aggregate messages via an inflexible pattern for various action samples, lacking the awareness of intra-class variety and the suitableness for skeleton sequences, which often contain redundant or even detrimental connections. In this paper, we propose a novel Deformable Graph Convolutional Network (DeGCN) to adaptively capture the most informative joints. The proposed DeGCN learns the deformable sampling locations on both spatial and temporal graphs, enabling the model to perceive discriminative receptive fields. Notably, considering human action is inherently continuous, the corresponding temporal features are defined in a continuous latent space. Furthermore, we design an innovative multi-branch framework, which not only strikes a better trade-off between accuracy and model size, but also elevates the effect of ensemble between the joint and bone modalities remarkably. Extensive experiments show that our proposed method achieves state-of-the-art performances on three widely used datasets, NTU RGB+D, NTU RGB+D 120, and NW-UCLA.
date: 2024
date_type: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
official_url: http://dx.doi.org/10.1109/tip.2024.3378886
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2262735
doi: 10.1109/tip.2024.3378886
lyricists_name: Xue, Jinghao
lyricists_id: JXUEX60
actors_name: Xue, Jinghao
actors_id: JXUEX60
actors_role: owner
full_text_status: public
publication: IEEE Transactions on Image Processing
citation:        Myung, Woomin;    Su, Nan;    Xue, Jing-Hao;    Wang, Guijin;      (2024)    DeGCN: Deformable Graph Convolutional Networks for Skeleton-Based Action Recognition.                   IEEE Transactions on Image Processing        10.1109/tip.2024.3378886 <https://doi.org/10.1109/tip.2024.3378886>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10189813/1/WoominMyung-TIP-2024.pdf