Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Crowd Counting Using Scale-Aware Attention Networks   In this paper, we consider the problem of crowd counting in images. Given an
image of a crowded scene, our goal is to estimate the density map of this
image, where each pixel value in the density map corresponds to the crowd
density at the corresponding location in the image. Given the estimated density
map, the final crowd count can be obtained by summing over all values in the
density map. One challenge of crowd counting is the scale variation in images.
In this work, we propose a novel scale-aware attention network to address this
challenge. Using the attention mechanism popular in recent deep learning
architectures, our model can automatically focus on certain global and local
scales appropriate for the image. By combining these global and local scale
attention, our model outperforms other state-of-the-art methods for crowd
counting on several benchmark datasets.

16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
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The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph