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

• URL: http://arxiv.org/abs/2104.04255v1
• Date: Fri, 9 Apr 2021 09:06:53 GMT
• Title: Skeleton-based Hand-Gesture Recognition with Lightweight Graph Convolutional Networks
• Authors: Hichem Sahbi
• Abstract summary: Graph convolutional networks (GCNs) aim at extending deep learning to arbitrary irregular domains, such as graphs. We introduce a novel method that learns the topology of input graphs as a part of GCN design. Experiments conducted on the challenging task of skeleton-based hand-gesture recognition show the high effectiveness of the learned GCNs.
• Score: 14.924672048447338
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited [51.24526202984846]
  Graph-based semi-supervised learning (GSSL) has long been a hot research topic. graph convolutional networks (GCNs) have become the predominant techniques for their promising performance.
  arXiv  Detail & Related papers  (2023-09-24T10:10:21Z)
• Towards Unsupervised Deep Graph Structure Learning [67.58720734177325]
  We propose an unsupervised graph structure learning paradigm, where the learned graph topology is optimized by data itself without any external guidance. Specifically, we generate a learning target from the original data as an "anchor graph", and use a contrastive loss to maximize the agreement between the anchor graph and the learned graph.
  arXiv  Detail & Related papers  (2022-01-17T11:57:29Z)
• Learning Connectivity with Graph Convolutional Networks for Skeleton-based Action Recognition [14.924672048447338]
  We introduce a novel framework for graph convolutional networks that learns the topological properties of graphs. The design principle of our method is based on the optimization of a constrained objective function. Experiments conducted on the challenging task of skeleton-based action recognition shows the superiority of the proposed method.
  arXiv  Detail & Related papers  (2021-12-06T19:43:26Z)
• Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network [12.008472517000651]
  We propose wGCN -- a novel framework that utilizes random walk to obtain the node-specific mesoscopic structures of the graph. We believe that combining high-order local structural information can more efficiently explore the potential of the network.
  arXiv  Detail & Related papers  (2021-01-02T10:31:14Z)
• Action Recognition with Kernel-based Graph Convolutional Networks [14.924672048447338]
  Learning graph convolutional networks (GCNs) aims at generalizing deep learning to arbitrary non-regular domains. We introduce a novel GCN framework that achieves spatial graph convolution in a reproducing kernel Hilbert space (RKHS) The particularity of our GCN model also resides in its ability to achieve convolutions without explicitly realigning nodes in the receptive fields of the learned graph filters with those of the input graphs.
  arXiv  Detail & Related papers  (2020-12-28T11:02:51Z)
• Spatio-Temporal Inception Graph Convolutional Networks for Skeleton-Based Action Recognition [126.51241919472356]
  We design a simple and highly modularized graph convolutional network architecture for skeleton-based action recognition. Our network is constructed by repeating a building block that aggregates multi-granularity information from both the spatial and temporal paths.
  arXiv  Detail & Related papers  (2020-11-26T14:43:04Z)
• Progressive Spatio-Temporal Graph Convolutional Network for Skeleton-Based Human Action Recognition [97.14064057840089]
  We propose a method to automatically find a compact and problem-specific network for graph convolutional networks in a progressive manner. Experimental results on two datasets for skeleton-based human action recognition indicate that the proposed method has competitive or even better classification performance.
  arXiv  Detail & Related papers  (2020-11-11T09:57:49Z)
• Representation Learning of Graphs Using Graph Convolutional Multilayer Networks Based on Motifs [17.823543937167848]
  mGCMN is a novel framework which utilizes node feature information and the higher order local structure of the graph. It will greatly improve the learning efficiency of the graph neural network and promote a brand-new learning mode establishment.
  arXiv  Detail & Related papers  (2020-07-31T04:18:20Z)
• Knowledge Embedding Based Graph Convolutional Network [35.35776808660919]
  This paper proposes a novel framework, namely the Knowledge Embedding based Graph Convolutional Network (KE-GCN) KE-GCN combines the power of Graph Convolutional Network (GCN) in graph-based belief propagation and the strengths of advanced knowledge embedding methods. Our theoretical analysis shows that KE-GCN offers an elegant unification of several well-known GCN methods as specific cases.
  arXiv  Detail & Related papers  (2020-06-12T17:12:51Z)
• Distilling Knowledge from Graph Convolutional Networks [146.71503336770886]
  Existing knowledge distillation methods focus on convolutional neural networks (CNNs) We propose the first dedicated approach to distilling knowledge from a pre-trained graph convolutional network (GCN) model. We show that our method achieves the state-of-the-art knowledge distillation performance for GCN models.
  arXiv  Detail & Related papers  (2020-03-23T18:23:11Z)
• Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions [153.6357310444093]
  Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data. We argue that existing designs of GCN forgo modeling cross features, making GCN less effective for tasks or data where cross features are important. We design a new operator named Cross-feature Graph Convolution, which explicitly models the arbitrary-order cross features with complexity linear to feature dimension and order size.
  arXiv  Detail & Related papers  (2020-03-05T13:05:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.