Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SuPEr-SAM: Using the Supervision Signal from a Pose Estimator to Train a
  Spatial Attention Module for Personal Protective Equipment Recognition   We propose a deep learning method to automatically detect personal protective
equipment (PPE), such as helmets, surgical masks, reflective vests, boots and
so on, in images of people. Typical approaches for PPE detection based on deep
learning are (i) to train an object detector for items such as those listed
above or (ii) to train a person detector and a classifier that takes the
bounding boxes predicted by the detector and discriminates between people
wearing and people not wearing the corresponding PPE items. We propose a novel
and accurate approach that uses three components: a person detector, a body
pose estimator and a classifier. Our novelty consists in using the pose
estimator only at training time, to improve the prediction performance of the
classifier. We modify the neural architecture of the classifier by adding a
spatial attention mechanism, which is trained using supervision signal from the
pose estimator. In this way, the classifier learns to focus on PPE items, using
knowledge from the pose estimator with almost no computational overhead during
inference.

10
The categories of this paper are: q-bio
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat