Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepMark++: Real-time Clothing Detection at the Edge   Clothing recognition is the most fundamental AI application challenge within
the fashion domain. While existing solutions offer decent recognition accuracy,
they are generally slow and require significant computational resources. In
this paper we propose a single-stage approach to overcome this obstacle and
deliver rapid clothing detection and keypoint estimation. Our solution is based
on a multi-target network CenterNet, and we introduce several powerful
post-processing techniques to enhance performance. Our most accurate model
achieves results comparable to state-of-the-art solutions on the DeepFashion2
dataset, and our light and fast model runs at 17 FPS on the Huawei P40 Pro
smartphone. In addition, we achieved second place in the DeepFashion2 Landmark
Estimation Challenge 2020 with 0.582 mAP on the test dataset.

31
The categories of this paper are: ao-sci
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
26
The categories of this paper are: bayes-an
27
The categories of this paper are: comp-gas