Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Blind Visual Motif Removal from a Single Image   Many images shared over the web include overlaid objects, or visual motifs,
such as text, symbols or drawings, which add a description or decoration to the
image. For example, decorative text that specifies where the image was taken,
repeatedly appears across a variety of different images. Often, the reoccurring
visual motif, is semantically similar, yet, differs in location, style and
content (e.g. text placement, font and letters). This work proposes a deep
learning based technique for blind removal of such objects. In the blind
setting, the location and exact geometry of the motif are unknown. Our approach
simultaneously estimates which pixels contain the visual motif, and synthesizes
the underlying latent image. It is applied to a single input image, without any
user assistance in specifying the location of the motif, achieving
state-of-the-art results for blind removal of both opaque and semi-transparent
visual motifs.

24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg