Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ReGO: Reference-Guided Outpainting for Scenery Image   We aim to tackle the challenging yet practical scenery image outpainting task
in this work. Recently, generative adversarial learning has significantly
advanced the image outpainting by producing semantic consistent content for the
given image. However, the existing methods always suffer from the blurry
texture and the artifacts of the generative part, making the overall
outpainting results lack authenticity. To overcome the weakness, this work
investigates a principle way to synthesize texture-rich results by borrowing
pixels from its neighbors (i.e., reference images), named
\textbf{Re}ference-\textbf{G}uided \textbf{O}utpainting (ReGO). Particularly,
the ReGO designs an Adaptive Content Selection (ACS) module to transfer the
pixel of reference images for texture compensating of the target one. To
prevent the style of the generated part from being affected by the reference
images, a style ranking loss is further proposed to augment the ReGO to
synthesize style-consistent results. Extensive experiments on two popular
benchmarks, NS6K \cite{yangzx} and NS8K \cite{wang}, well demonstrate the
effectiveness of our ReGO. Our code will be made public available.

23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
2
The categories of this paper are: math
9
The categories of this paper are: nlin
35
The categories of this paper are: mtrl-th
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an