Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Scalable Annotation of Fine-Grained Categories Without Experts   We present a crowdsourcing workflow to collect image annotations for visually
similar synthetic categories without requiring experts. In animals, there is a
direct link between taxonomy and visual similarity: e.g. a collie (type of dog)
looks more similar to other collies (e.g. smooth collie) than a greyhound
(another type of dog). However, in synthetic categories such as cars, objects
with similar taxonomy can have very different appearance: e.g. a 2011 Ford
F-150 Supercrew-HD looks the same as a 2011 Ford F-150 Supercrew-LL but very
different from a 2011 Ford F-150 Supercrew-SVT. We introduce a graph based
crowdsourcing algorithm to automatically group visually indistinguishable
objects together. Using our workflow, we label 712,430 images by ~1,000 Amazon
Mechanical Turk workers; resulting in the largest fine-grained visual dataset
reported to date with 2,657 categories of cars annotated at 1/20th the cost of
hiring experts.

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The categories of this paper are: funct-an
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The categories of this paper are: econ
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The categories of this paper are: comp-gas
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The categories of this paper are: quant-ph
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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: adap-org
33
The categories of this paper are: plasm-ph
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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: cond-mat
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-th
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The categories of this paper are: chem-ph
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The categories of this paper are: math