mlsanityData to 7 Oct 2026

Attention-based Graph Convolution Fusing Latent Structures and Multiple Features for Graph Neural Networks

arXiv 2303.00944 ↗First posted 2 Mar 2023cs.CV cs.AIcited by 0

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Abstract (arXiv)

We present an attention-based spatial graph convolution (AGC) for graph neural networks (GNNs). Existing AGCs focus on only using node-wise features and utilizing one type of attention function when calculating attention weights. Instead, we propose two methods to improve the representational power of AGCs by utilizing 1) structural information in a high-dimensional space and 2) multiple attention functions when calculating their weights. The first method computes a local structure representation of a graph in a high-dimensional space. The second method utilizes multiple attention functions simultaneously in one AGC. Both approaches can be combined. We also propose a GNN for the classification of point clouds and that for the prediction of point labels in a point cloud based on the proposed AGC. According to experiments, the proposed GNNs perform better than existing methods. Our codes open at https://github.com/liyang-tuat/SFAGC.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 40 of its 54 references that are in the map. First posted on arXiv 2 Mar 2023.

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40 of 54 references.

References · 40

  1. 2017Attention Is All You Needcited by 59,932
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  12. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
28 more
  1. 2016Convolutional Neural Networks on Graphs with Fast Localized Spectral Filteringcited by 2,085
  2. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  3. 2020Point Transformercited by 1,462
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  5. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  6. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  7. 2016Stacked Hourglass Networks for Human Pose Estimationcited by 913
  8. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  9. 2020PCT: Point cloud transformercited by 730
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  15. 2020PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Samplingcited by 238
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  17. 2019Grid-GCN for Fast and Scalable Point Cloud Learningcited by 103
  18. 2019Unsupervised Multi-Task Feature Learning on Point Cloudscited by 89
  19. 2019Interpolated Convolutional Networks for 3D Point Cloud Understandingcited by 86
  20. 2018Local Spectral Graph Convolution for Point Set Feature Learningcited by 84
  21. 2018RGCNN: Regularized Graph CNN for Point Cloud Segmentationcited by 70
  22. 2019A-CNN: Annularly Convolutional Neural Networks on Point Cloudscited by 66
  23. 2019ConvPoint: Continuous Convolutions for Point Cloud Processingcited by 62
  24. 2018A Graph-CNN for 3D Point Cloud Classificationcited by 49
  25. 2018Graph Spectral Image Processingcited by 41
  26. 2019Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Featurescited by 31
  27. 20183DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCNcited by 7
  28. 2021Structure-Aware Multi-Hop Graph Convolution for Graph Neural Networkscited by 1

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