Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Dual Path Networks for Multi-Person Human Pose Estimation   The task of multi-person human pose estimation in natural scenes is quite
challenging. Existing methods include both top-down and bottom-up approaches.
The main advantage of bottom-up methods is its excellent tradeoff between
estimation accuracy and computational cost. We follow this path and aim to
design smaller, faster, and more accurate neural networks for the regression of
keypoints and limb association vectors. These two regression tasks are
naturally dependent on each other. In this work, we propose a dual-path network
specially designed for multi-person human pose estimation, and compare our
performance with the openpose network in aspects of model size, forward speed,
and estimation accuracy.

11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
19
The categories of this paper are: eess
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn