Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Central Difference Graph Convolutional Operator for Skeleton-Based
  Action Recognition   This paper proposes a new graph convolutional operator called central
difference graph convolution (CDGC) for skeleton based action recognition. It
is not only able to aggregate node information like a vanilla graph
convolutional operation but also gradient information. Without introducing any
additional parameters, CDGC can replace vanilla graph convolution in any
existing Graph Convolutional Networks (GCNs). In addition, an accelerated
version of the CDGC is developed which greatly improves the speed of training.
Experiments on two popular large-scale datasets NTU RGB+D 60 & 120 have
demonstrated the efficacy of the proposed CDGC. Code is available at
https://github.com/iesymiao/CD-GCN.

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The categories of this paper are: hep-ph
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
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The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
15
The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys