Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Photozilla: A Large-Scale Photography Dataset and Visual Embedding for
  20 Photography Styles   The advent of social media platforms has been a catalyst for the development
of digital photography that engendered a boom in vision applications. With this
motivation, we introduce a large-scale dataset termed 'Photozilla', which
includes over 990k images belonging to 10 different photographic styles. The
dataset is then used to train 3 classification models to automatically classify
the images into the relevant style which resulted in an accuracy of ~96%. With
the rapid evolution of digital photography, we have seen new types of
photography styles emerging at an exponential rate. On that account, we present
a novel Siamese-based network that uses the trained classification models as
the base architecture to adapt and classify unseen styles with only 25 training
samples. We report an accuracy of over 68% for identifying 10 other distinct
types of photography styles. This dataset can be found at
https://trisha025.github.io/Photozilla/

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The categories of this paper are: alg-geom
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
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The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: astro-ph
2
The categories of this paper are: math
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The categories of this paper are: chem-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat