Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extracting associations and meanings of objects depicted in artworks
  through bi-modal deep networks   We present a novel bi-modal system based on deep networks to address the
problem of learning associations and simple meanings of objects depicted in
"authored" images, such as fine art paintings and drawings. Our overall system
processes both the images and associated texts in order to learn associations
between images of individual objects, their identities and the abstract
meanings they signify. Unlike past deep nets that describe depicted objects and
infer predicates, our system identifies meaning-bearing objects ("signifiers")
and their associations ("signifieds") as well as basic overall meanings for
target artworks. Our system had precision of 48% and recall of 78% with an F1
metric of 0.6 on a curated set of Dutch vanitas paintings, a genre celebrated
for its concentration on conveying a meaning of great import at the time of
their execution. We developed and tested our system on fine art paintings but
our general methods can be applied to other authored images.

21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
9
The categories of this paper are: nlin
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The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
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The categories of this paper are: funct-an
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The categories of this paper are: bayes-an
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
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The categories of this paper are: plasm-ph