Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CoCosNet v2: Full-Resolution Correspondence Learning for Image
  Translation   We present the full-resolution correspondence learning for cross-domain
images, which aids image translation. We adopt a hierarchical strategy that
uses the correspondence from coarse level to guide the fine levels. At each
hierarchy, the correspondence can be efficiently computed via PatchMatch that
iteratively leverages the matchings from the neighborhood. Within each
PatchMatch iteration, the ConvGRU module is employed to refine the current
correspondence considering not only the matchings of larger context but also
the historic estimates. The proposed CoCosNet v2, a GRU-assisted PatchMatch
approach, is fully differentiable and highly efficient. When jointly trained
with image translation, full-resolution semantic correspondence can be
established in an unsupervised manner, which in turn facilitates the
exemplar-based image translation. Experiments on diverse translation tasks show
that CoCosNet v2 performs considerably better than state-of-the-art literature
on producing high-resolution images.

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The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
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The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
2
The categories of this paper are: math
9
The categories of this paper are: nlin
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The categories of this paper are: econ
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio