Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Facial Pose Estimation by Deep Learning from Label Distributions   Facial pose estimation has gained a lot of attentions in many practical
applications, such as human-robot interaction, gaze estimation and driver
monitoring. Meanwhile, end-to-end deep learning-based facial pose estimation is
becoming more and more popular. However, facial pose estimation suffers from a
key challenge: the lack of sufficient training data for many poses, especially
for large poses. Inspired by the observation that the faces under close poses
look similar, we reformulate the facial pose estimation as a label distribution
learning problem, considering each face image as an example associated with a
Gaussian label distribution rather than a single label, and construct a
convolutional neural network which is trained with a multi-loss function on
AFLW dataset and 300W-LP dataset to predict the facial poses directly from
color image. Extensive experiments are conducted on several popular benchmarks,
including AFLW2000, BIWI, AFLW and AFW, where our approach shows a significant
advantage over other state-of-the-art methods.

24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph