Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Strong Baseline for Crowd Counting and Unsupervised People
  Localization   In this paper, we explore a strong baseline for crowd counting and an
unsupervised people localization algorithm based on estimated density maps.
Firstly, existing methods achieve state-of-the-art performance based on
different backbones and kinds of training tricks. We collect different
backbones and training tricks and evaluate the impact of changing them and
develop an efficient pipeline for crowd counting, which decreases MAE and RMSE
significantly on multiple datasets. We also propose a clustering algorithm
named isolated KMeans to locate the heads in density maps. This method can
divide the density maps into subregions and find the centers under local count
constraints without training any parameter and can be integrated with existing
methods easily.

36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org