Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Heterogeneous Multi-task Learning for Human Pose Estimation with Deep
  Convolutional Neural Network   We propose an heterogeneous multi-task learning framework for human pose
estimation from monocular image with deep convolutional neural network. In
particular, we simultaneously learn a pose-joint regressor and a sliding-window
body-part detector in a deep network architecture. We show that including the
body-part detection task helps to regularize the network, directing it to
converge to a good solution. We report competitive and state-of-art results on
several data sets. We also empirically show that the learned neurons in the
middle layer of our network are tuned to localized body parts.

12
The categories of this paper are: nucl-th
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph