Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatic Semantic Content Removal by Learning to Neglect   We introduce a new system for automatic image content removal and inpainting.
Unlike traditional inpainting algorithms, which require advance knowledge of
the region to be filled in, our system automatically detects the area to be
removed and infilled. Region segmentation and inpainting are performed jointly
in a single pass. In this way, potential segmentation errors are more naturally
alleviated by the inpainting module. The system is implemented as an
encoder-decoder architecture, with two decoder branches, one tasked with
segmentation of the foreground region, the other with inpainting. The encoder
and the two decoder branches are linked via neglect nodes, which guide the
inpainting process in selecting which areas need reconstruction. The whole
model is trained using a conditional GAN strategy. Comparative experiments show
that our algorithm outperforms state-of-the-art inpainting techniques (which,
unlike our system, do not segment the input image and thus must be aided by an
external segmentation module.)

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The categories of this paper are: cond-mat
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
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The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
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The categories of this paper are: alg-geom
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The categories of this paper are: physics
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The categories of this paper are: gr-qc
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The categories of this paper are: math
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The categories of this paper are: acc-phys
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th