Corpus ID: 198147831

Image Classification with Hierarchical Multigraph Networks

@inproceedings{Knyazev2019ImageCW,
  title={Image Classification with Hierarchical Multigraph Networks},
  author={Boris Knyazev and X. Lin and M. Amer and Graham W. Taylor},
  booktitle={BMVC},
  year={2019}
}
  • Boris Knyazev, X. Lin, +1 author Graham W. Taylor
  • Published in BMVC 2019
  • Computer Science
  • Graph Convolutional Networks (GCNs) are a class of general models that can learn from graph structured data. [...] Key Result Building upon these two promising properties, in this work, we show best practices for designing GCNs for image classification; in some cases even outperforming CNNs on the MNIST, CIFAR-10 and PASCAL image datasets.Expand Abstract

    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 39 REFERENCES
    Experience in the surgical management of spontaneous spinal epidural hematoma.
    98
    Some features of children’s ideas and their implications for teaching (pp. 193-201)
    • 1985
    Some identities of the twisted q-Euler numbers and polynomials associated with q-Bernstein polynomials
    • 2011
    Real time 3D face alignment with Random Forests-based Active Appearance Models
    47
    Signal Processing in Cognitive Radio
    554