Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks

04/09/2021
by   Hichem Sahbi, et al.
0

Graph convolutional networks (GCNs) aim at extending deep learning to arbitrary irregular domains, namely graphs. Their success is highly dependent on how the topology of input graphs is defined and most of the existing GCN architectures rely on predefined or handcrafted graph structures. In this paper, we introduce a novel method that learns the topology (or connectivity) of input graphs as a part of GCN design. The main contribution of our method resides in building an orthogonal connectivity basis that optimally aggregates nodes, through their neighborhood, prior to achieve convolution. Our method also considers a stochasticity criterion which acts as a regularizer that makes the learned basis and the underlying GCNs lightweight while still being highly effective. Experiments conducted on the challenging task of skeleton-based hand-gesture recognition show the high effectiveness of the learned GCNs w.r.t. the related work.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/06/2021

Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition

Learning graph convolutional networks (GCNs) is an emerging field which ...
research
04/12/2021

Learning Chebyshev Basis in Graph Convolutional Networks for Skeleton-based Action Recognition

Spectral graph convolutional networks (GCNs) are particular deep models ...
research
12/28/2020

Action Recognition with Kernel-based Graph Convolutional Networks

Learning graph convolutional networks (GCNs) is an emerging field which ...
research
12/19/2022

Training Lightweight Graph Convolutional Networks with Phase-field Models

In this paper, we design lightweight graph convolutional networks (GCNs)...
research
10/15/2019

Human Action Recognition with Multi-Laplacian Graph Convolutional Networks

Convolutional neural networks are nowadays witnessing a major success in...
research
03/25/2022

Lightweight Graph Convolutional Networks with Topologically Consistent Magnitude Pruning

Graph convolution networks (GCNs) are currently mainstream in learning w...
research
05/30/2023

Budget-Aware Graph Convolutional Network Design using Probabilistic Magnitude Pruning

Graph convolutional networks (GCNs) are nowadays becoming mainstream in ...

Please sign up or login with your details

Forgot password? Click here to reset