Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Detecting People in Artwork with CNNs   CNNs have massively improved performance in object detection in photographs.
However research into object detection in artwork remains limited. We show
state-of-the-art performance on a challenging dataset, People-Art, which
contains people from photos, cartoons and 41 different artwork movements. We
achieve this high performance by fine-tuning a CNN for this task, thus also
demonstrating that training CNNs on photos results in overfitting for photos:
only the first three or four layers transfer from photos to artwork. Although
the CNN's performance is the highest yet, it remains less than 60\% AP,
suggesting further work is needed for the cross-depiction problem. The final
publication is available at Springer via
http://dx.doi.org/10.1007/978-3-319-46604-0_57

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The categories of this paper are: math
36
The categories of this paper are: cmp-lg
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
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The categories of this paper are: adap-org
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-fin