Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fine-grained Recognition Datasets for Biodiversity Analysis   In the following paper, we present and discuss challenging applications for
fine-grained visual classification (FGVC): biodiversity and species analysis.
We not only give details about two challenging new datasets suitable for
computer vision research with up to 675 highly similar classes, but also
present first results with localized features using convolutional neural
networks (CNN). We conclude with a list of challenging new research directions
in the area of visual classification for biodiversity research.

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The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
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The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
2
The categories of this paper are: math
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The categories of this paper are: ao-sci
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The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
12
The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: bayes-an