Human Action Recognition with Multi-Laplacian Graph Convolutional Networks
This work addresses the problem of handling variable graph structures in pattern recognition for researchers in computer vision and graph learning, though it appears incremental as it builds on existing graph convolutional methods.
The paper tackles the challenge of extending convolutional neural networks to non-vectorial graph data by introducing MLGCN, a spectral Multi-Laplacian Graph Convolutional Network, which achieves improved performance on action recognition tasks using SBU and UCF-101 datasets.
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.