Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: HyperFace: A Deep Multi-task Learning Framework for Face Detection,
  Landmark Localization, Pose Estimation, and Gender Recognition   We present an algorithm for simultaneous face detection, landmarks
localization, pose estimation and gender recognition using deep convolutional
neural networks (CNN). The proposed method called, HyperFace, fuses the
intermediate layers of a deep CNN using a separate CNN followed by a multi-task
learning algorithm that operates on the fused features. It exploits the synergy
among the tasks which boosts up their individual performances. Additionally, we
propose two variants of HyperFace: (1) HyperFace-ResNet that builds on the
ResNet-101 model and achieves significant improvement in performance, and (2)
Fast-HyperFace that uses a high recall fast face detector for generating region
proposals to improve the speed of the algorithm. Extensive experiments show
that the proposed models are able to capture both global and local information
in faces and performs significantly better than many competitive algorithms for
each of these four tasks.

16
The categories of this paper are: stat
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th