Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stacked Hourglass Networks for Human Pose Estimation   This work introduces a novel convolutional network architecture for the task
of human pose estimation. Features are processed across all scales and
consolidated to best capture the various spatial relationships associated with
the body. We show how repeated bottom-up, top-down processing used in
conjunction with intermediate supervision is critical to improving the
performance of the network. We refer to the architecture as a "stacked
hourglass" network based on the successive steps of pooling and upsampling that
are done to produce a final set of predictions. State-of-the-art results are
achieved on the FLIC and MPII benchmarks outcompeting all recent methods.

31
The categories of this paper are: ao-sci
16
The categories of this paper are: stat
14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th