Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Captioning Images Taken by People Who Are Blind   While an important problem in the vision community is to design algorithms
that can automatically caption images, few publicly-available datasets for
algorithm development directly address the interests of real users. Observing
that people who are blind have relied on (human-based) image captioning
services to learn about images they take for nearly a decade, we introduce the
first image captioning dataset to represent this real use case. This new
dataset, which we call VizWiz-Captions, consists of over 39,000 images
originating from people who are blind that are each paired with five captions.
We analyze this dataset to (1) characterize the typical captions, (2)
characterize the diversity of content found in the images, and (3) compare its
content to that found in eight popular vision datasets. We also analyze modern
image captioning algorithms to identify what makes this new dataset challenging
for the vision community. We publicly-share the dataset with captioning
challenge instructions at https://vizwiz.org

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The categories of this paper are: atom-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: eess
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The categories of this paper are: astro-ph
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The categories of this paper are: adap-org
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: funct-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-fin
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The categories of this paper are: gr-qc
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The categories of this paper are: q-bio
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The categories of this paper are: comp-gas
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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: math