Chapter20: Graph Neural Networks in Computer Vision

Siliang Tang, Zhejiang University, siliang@zju.edu.cn
Wenqiao Zhang, Zhejiang University, wenqiaozhang@zju.edu.cn
Zongshen Mu, Zhejiang University, zongshen@zju.edu.cn
Kai Shen, Zhejiang University, shenkai@zju.edu.cn
Juncheng Li, Zhejiang University, junchengli@zju.edu.cn
Jiacheng Li, Zhejiang University, lijiacheng@zju.edu.cn
Lingfei Wu, JD.COM Silicon Valley Research Center, lwu@email.wm.edu

Abstract

Recently Graph Neural Networks (GNNs) have been incorporated into many Computer Vision (CV) models. They not only bring performance improvement to many CV-related tasks but also provide more explainable decomposition to these CV models. This chapter provides a comprehensive overview of how GNNs are applied to various CV tasks, ranging from single image classification to crossmedia understanding. It also provides a discussion of this rapidly growing field from a frontier perspective.

Contents

  • Introduction
  • Representing Visions as Graphs
    • Visual Node representation
    • Visual Edge representation
  • Case Study 1: Image
    • Object Detection
    • Image Classification
  • Case Study 2: Video
    • Video Action Recognition
    • Temporal Action Localization
  • Other Related Work: Cross-media
    • Vision Caption
    • Visual Question Answering
    • Cross-Media Retrieval
  • Frontiers for GNNs on Computer Vision
    • Advanced GNN Modeling Methods for Computer Vision
    • Broader Area of GNNs on Computer Vision
  • Summary

Citation

@incollection{GNNBook-ch20-wu,
author = "Tang, Siliang and Zhang, Wenqiao and Mu, Zongshen and Shen, Kai and Li, Juncheng and Li, Jiacheng and Wu, Lingfei",
editor = "Wu, Lingfei and Cui, Peng and Pei, Jian and Zhao, Liang",
title = "Graph Neural Networks in Computer Vision",
booktitle = "Graph Neural Networks: Foundations, Frontiers, and Applications",
year = "2022",
publisher = "Springer Singapore",
address = "Singapore",
pages = "447--462",
}

S. Tang, W. Zhang, Z. Mu, K. Shen, J. Li, J. Li, and L. Wu, “Graph neural networks in computer vision,” in Graph Neural Networks: Foundations, Frontiers, and Applications, L. Wu, P. Cui, J. Pei, and L. Zhao, Eds. Singapore: Springer Singapore, 2022, pp. 447–462.