Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ASFD: Automatic and Scalable Face Detector   In this paper, we propose a novel Automatic and Scalable Face Detector
(ASFD), which is based on a combination of neural architecture search
techniques as well as a new loss design. First, we propose an automatic feature
enhance module named Auto-FEM by improved differential architecture search,
which allows efficient multi-scale feature fusion and context enhancement.
Second, we use Distance-based Regression and Margin-based Classification (DRMC)
multi-task loss to predict accurate bounding boxes and learn highly
discriminative deep features. Third, we adopt compound scaling methods and
uniformly scale the backbone, feature modules, and head networks to develop a
family of ASFD, which are consistently more efficient than the state-of-the-art
face detectors. Extensive experiments conducted on popular benchmarks, e.g.
WIDER FACE and FDDB, demonstrate that our ASFD-D6 outperforms the prior strong
competitors, and our lightweight ASFD-D0 runs at more than 120 FPS with
Mobilenet for VGA-resolution images.

26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat