Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Image Aesthetics Prediction Using Multiple Patches Preserving the
  Original Aspect Ratio of Contents   The spread of social networking services has created an increasing demand for
selecting, editing, and generating impressive images. This trend increases the
importance of evaluating image aesthetics as a complementary function of
automatic image processing. We propose a multi-patch method, named MPA-Net
(Multi-Patch Aggregation Network), to predict image aesthetics scores by
maintaining the original aspect ratios of contents in the images. Through an
experiment involving the large-scale AVA dataset, which contains 250,000
images, we show that the effectiveness of the equal-interval multi-patch
selection approach for aesthetics score prediction is significant compared to
the single-patch prediction and random patch selection approaches. For this
dataset, MPA-Net outperforms the neural image assessment algorithm, which was
regarded as a baseline method. In particular, MPA-Net yields a 0.073 (11.5%)
higher linear correlation coefficient (LCC) of aesthetics scores and a 0.088
(14.4%) higher Spearman's rank correlation coefficient (SRCC). MPA-Net also
reduces the mean square error (MSE) by 0.0115 (4.18%) and achieves results for
the LCC and SRCC that are comparable to those of the state-of-the-art
continuous aesthetics score prediction methods. Most notably, MPA-Net yields a
significant lower MSE especially for images with aspect ratios far from 1.0,
indicating that MPA-Net is useful for a wide range of image aspect ratios.
MPA-Net uses only images and does not require external information during the
training nor prediction stages. Therefore, MPA-Net has great potential for
applications aside from aesthetics score prediction such as other human
subjectivity prediction.

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The categories of this paper are: atom-ph
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The categories of this paper are: q-fin
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The categories of this paper are: stat
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The categories of this paper are: math
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The categories of this paper are: nucl-ex
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: q-bio
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The categories of this paper are: econ
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The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org