Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semantic Graph Convolutional Networks for 3D Human Pose Regression   In this paper, we study the problem of learning Graph Convolutional Networks
(GCNs) for regression. Current architectures of GCNs are limited to the small
receptive field of convolution filters and shared transformation matrix for
each node. To address these limitations, we propose Semantic Graph
Convolutional Networks (SemGCN), a novel neural network architecture that
operates on regression tasks with graph-structured data. SemGCN learns to
capture semantic information such as local and global node relationships, which
is not explicitly represented in the graph. These semantic relationships can be
learned through end-to-end training from the ground truth without additional
supervision or hand-crafted rules. We further investigate applying SemGCN to 3D
human pose regression. Our formulation is intuitive and sufficient since both
2D and 3D human poses can be represented as a structured graph encoding the
relationships between joints in the skeleton of a human body. We carry out
comprehensive studies to validate our method. The results prove that SemGCN
outperforms state of the art while using 90% fewer parameters.

7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
37
The categories of this paper are: supr-con