Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Human Action Recognition with Multi-Laplacian Graph Convolutional
  Networks   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.

12
The categories of this paper are: nucl-th
2
The categories of this paper are: math
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ