Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual Question Answering Using Semantic Information from Image
  Descriptions   In this work, we propose a deep neural architecture that uses an attention
mechanism which utilizes region based image features, the natural language
question asked, and semantic knowledge extracted from the regions of an image
to produce open-ended answers for questions asked in a visual question
answering (VQA) task. The combination of both region based features and region
based textual information about the image bolsters a model to more accurately
respond to questions and potentially do so with less required training data. We
evaluate our proposed architecture on a VQA task against a strong baseline and
show that our method achieves excellent results on this task.

31
The categories of this paper are: ao-sci
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an
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The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
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The categories of this paper are: math-ph
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
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The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin