Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose
  Estimation   This paper focuses on structured-output learning using deep neural networks
for 3D human pose estimation from monocular images. Our network takes an image
and 3D pose as inputs and outputs a score value, which is high when the
image-pose pair matches and low otherwise. The network structure consists of a
convolutional neural network for image feature extraction, followed by two
sub-networks for transforming the image features and pose into a joint
embedding. The score function is then the dot-product between the image and
pose embeddings. The image-pose embedding and score function are jointly
trained using a maximum-margin cost function. Our proposed framework can be
interpreted as a special form of structured support vector machines where the
joint feature space is discriminatively learned using deep neural networks. We
test our framework on the Human3.6m dataset and obtain state-of-the-art results
compared to other recent methods. Finally, we present visualizations of the
image-pose embedding space, demonstrating the network has learned a high-level
embedding of body-orientation and pose-configuration.

18
The categories of this paper are: econ
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex