Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Human Attention in Visual Question Answering: Do Humans and Deep
  Networks Look at the Same Regions?   We conduct large-scale studies on `human attention' in Visual Question
Answering (VQA) to understand where humans choose to look to answer questions
about images. We design and test multiple game-inspired novel
attention-annotation interfaces that require the subject to sharpen regions of
a blurred image to answer a question. Thus, we introduce the VQA-HAT (Human
ATtention) dataset. We evaluate attention maps generated by state-of-the-art
VQA models against human attention both qualitatively (via visualizations) and
quantitatively (via rank-order correlation). Overall, our experiments show that
current attention models in VQA do not seem to be looking at the same regions
as humans.

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The categories of this paper are: cmp-lg
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The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: physics
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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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: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: mtrl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: supr-con
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The categories of this paper are: nlin