Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Text-guided Attention Model for Image Captioning   Visual attention plays an important role to understand images and
demonstrates its effectiveness in generating natural language descriptions of
images. On the other hand, recent studies show that language associated with an
image can steer visual attention in the scene during our cognitive process.
Inspired by this, we introduce a text-guided attention model for image
captioning, which learns to drive visual attention using associated captions.
For this model, we propose an exemplar-based learning approach that retrieves
from training data associated captions with each image, and use them to learn
attention on visual features. Our attention model enables to describe a
detailed state of scenes by distinguishing small or confusable objects
effectively. We validate our model on MS-COCO Captioning benchmark and achieve
the state-of-the-art performance in standard metrics.

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The categories of this paper are: quant-ph
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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: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: patt-sol
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an
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The categories of this paper are: nucl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: supr-con
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The categories of this paper are: stat
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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph