Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adapted Center and Scale Prediction: More Stable and More Accurate   Pedestrian detection benefits from deep learning technology and gains rapid
development in recent years. Most of detectors follow general object detection
frame, i.e. default boxes and two-stage process. Recently, anchor-free and
one-stage detectors have been introduced into this area. However, their
accuracies are unsatisfactory. Therefore, in order to enjoy the simplicity of
anchor-free detectors and the accuracy of two-stage ones simultaneously, we
propose some adaptations based on a detector, Center and Scale Prediction(CSP).
The main contributions of our paper are: (1) We improve the robustness of CSP
and make it easier to train. (2) We propose a novel method to predict width,
namely compressing width. (3) We achieve the second best performance on
CityPersons benchmark, i.e. 9.3% log-average miss rate(MR) on reasonable set,
8.7% MR on partial set and 5.6% MR on bare set, which shows an anchor-free and
one-stage detector can still have high accuracy. (4) We explore some
capabilities of Switchable Normalization which are not mentioned in its
original paper.

15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
6
The categories of this paper are: astro-ph
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con