Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: simNet: Stepwise Image-Topic Merging Network for Generating Detailed and
  Comprehensive Image Captions   The encode-decoder framework has shown recent success in image captioning.
Visual attention, which is good at detailedness, and semantic attention, which
is good at comprehensiveness, have been separately proposed to ground the
caption on the image. In this paper, we propose the Stepwise Image-Topic
Merging Network (simNet) that makes use of the two kinds of attention at the
same time. At each time step when generating the caption, the decoder
adaptively merges the attentive information in the extracted topics and the
image according to the generated context, so that the visual information and
the semantic information can be effectively combined. The proposed approach is
evaluated on two benchmark datasets and reaches the state-of-the-art
performances.(The code is available at https://github.com/lancopku/simNet)

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The categories of this paper are: astro-ph
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The categories of this paper are: math-ph
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The categories of this paper are: nlin
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The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: eess
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an