Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: BargainNet: Background-Guided Domain Translation for Image Harmonization   Image composition is a fundamental operation in image editing field. However,
unharmonious foreground and background downgrade the quality of composite
image. Image harmonization, which adjusts the foreground to improve the
consistency, is an essential yet challenging task. Previous deep learning based
methods mainly focus on directly learning the mapping from composite image to
real image, while ignoring the crucial guidance role that background plays. In
this work, with the assumption that the foreground needs to be translated to
the same domain as background, we formulate image harmonization task as
background-guided domain translation. Therefore, we propose an image
harmonization network with a novel domain code extractor and well-tailored
triplet losses, which could capture the background domain information to guide
the foreground harmonization. Extensive experiments on the existing image
harmonization benchmark demonstrate the effectiveness of our proposed method.
Code is available at https://github.com/bcmi/BargainNet.

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The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: mtrl-th
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The categories of this paper are: ao-sci
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The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
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The categories of this paper are: stat
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
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The categories of this paper are: supr-con
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: eess
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The categories of this paper are: comp-gas
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The categories of this paper are: physics
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The categories of this paper are: acc-phys