Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SemStyle: Learning to Generate Stylised Image Captions using Unaligned
  Text   Linguistic style is an essential part of written communication, with the
power to affect both clarity and attractiveness. With recent advances in vision
and language, we can start to tackle the problem of generating image captions
that are both visually grounded and appropriately styled. Existing approaches
either require styled training captions aligned to images or generate captions
with low relevance. We develop a model that learns to generate visually
relevant styled captions from a large corpus of styled text without aligned
images. The core idea of this model, called SemStyle, is to separate semantics
and style. One key component is a novel and concise semantic term
representation generated using natural language processing techniques and frame
semantics. In addition, we develop a unified language model that decodes
sentences with diverse word choices and syntax for different styles.
Evaluations, both automatic and manual, show captions from SemStyle preserve
image semantics, are descriptive, and are style shifted. More broadly, this
work provides possibilities to learn richer image descriptions from the
plethora of linguistic data available on the web.

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The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
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The categories of this paper are: plasm-ph
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: physics
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The categories of this paper are: hep-th
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin