Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiple Exemplars-based Hallucinationfor Face Super-resolution and
  Editing   Given a really low-resolution input image of a face (say 16x16 or 8x8
pixels), the goal of this paper is to reconstruct a high-resolution version
thereof. This, by itself, is an ill-posed problem, as the high-frequency
information is missing in the low-resolution input and needs to be
hallucinated, based on prior knowledge about the image content. Rather than
relying on a generic face prior, in this paper, we explore the use of a set of
exemplars, i.e. other high-resolution images of the same person. These guide
the neural network as we condition the output on them. Multiple exemplars work
better than a single one. To combine the information from multiple exemplars
effectively, we introduce a pixel-wise weight generation module. Besides
standard face super-resolution, our method allows to perform subtle face
editing simply by replacing the exemplars with another set with different
facial features. A user study is conducted and shows the super-resolved images
can hardly be distinguished from real images on the CelebA dataset. A
qualitative comparison indicates our model outperforms methods proposed in the
literature on the CelebA and WebFace dataset.

17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: econ
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
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The categories of this paper are: cond-mat
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The categories of this paper are: comp-gas