Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantifying the amount of visual information used by neural caption
  generators   This paper addresses the sensitivity of neural image caption generators to
their visual input. A sensitivity analysis and omission analysis based on image
foils is reported, showing that the extent to which image captioning
architectures retain and are sensitive to visual information varies depending
on the type of word being generated and the position in the caption as a whole.
We motivate this work in the context of broader goals in the field to achieve
more explainability in AI.

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The categories of this paper are: cond-mat
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
3
The categories of this paper are: physics
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: adap-org
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The categories of this paper are: astro-ph
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The categories of this paper are: math
32
The categories of this paper are: atom-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: q-bio
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The categories of this paper are: stat
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The categories of this paper are: cmp-lg