{
  "id": "1910.06934",
  "version": "v1",
  "published": "2019-10-15T17:15:55.000Z",
  "updated": "2019-10-15T17:15:55.000Z",
  "title": "Human Action Recognition with Multi-Laplacian Graph Convolutional Networks",
  "authors": [
    "Ahmed Mazari",
    "Hichem Sahbi"
  ],
  "categories": [
    "cs.CV"
  ],
  "abstract": "Convolutional neural networks are nowadays witnessing a major success in different pattern recognition problems. These learning models were basically designed to handle vectorial data such as images but their extension to non-vectorial and semi-structured data (namely graphs with variable sizes, topology, etc.) remains a major challenge, though a few interesting solutions are currently emerging. In this paper, we introduce MLGCN; a novel spectral Multi-Laplacian Graph Convolutional Network. The main contribution of this method resides in a new design principle that learns graph-laplacians as convex combinations of other elementary laplacians each one dedicated to a particular topology of the input graphs. We also introduce a novel pooling operator, on graphs, that proceeds in two steps: context-dependent node expansion is achieved, followed by a global average pooling; the strength of this two-step process resides in its ability to preserve the discrimination power of nodes while achieving permutation invariance. Experiments conducted on SBU and UCF-101 datasets, show the validity of our method for the challenging task of action recognition.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-10-15T17:15:55.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "human action recognition",
      "spectral multi-laplacian graph convolutional network",
      "novel spectral multi-laplacian graph convolutional",
      "handle vectorial data",
      "convolutional neural networks"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}