Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Real-Time Head Pose Estimation: Exploring Parameter-Reduced
  Residual Networks on In-the-wild Datasets   Head poses are a key component of human bodily communication and thus a
decisive element of human-computer interaction. Real-time head pose estimation
is crucial in the context of human-robot interaction or driver assistance
systems. The most promising approaches for head pose estimation are based on
Convolutional Neural Networks (CNNs). However, CNN models are often too complex
to achieve real-time performance. To face this challenge, we explore a popular
subgroup of CNNs, the Residual Networks (ResNets) and modify them in order to
reduce their number of parameters. The ResNets are modifed for different image
sizes including low-resolution images and combined with a varying number of
layers. They are trained on in-the-wild datasets to ensure real-world
applicability. As a result, we demonstrate that the performance of the ResNets
can be maintained while reducing the number of parameters. The modified ResNets
achieve state-of-the-art accuracy and provide fast inference for real-time
applicability.

10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
27
The categories of this paper are: comp-gas