Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Artistic Instance-Aware Image Filtering by Convolutional Neural Networks   In the recent years, public use of artistic effects for editing and
beautifying images has encouraged researchers to look for new approaches to
this task. Most of the existing methods apply artistic effects to the whole
image. Exploitation of neural network vision technologies like object detection
and semantic segmentation could be a new viewpoint in this area. In this paper,
we utilize an instance segmentation neural network to obtain a class mask for
separately filtering the background and foreground of an image. We implement a
top prior-mask selection to let us select an object class for filtering
purpose. Different artistic effects are used in the filtering process to meet
the requirements of a vast variety of users. Also, our method is flexible
enough to allow the addition of new filters. We use pre-trained Mask R-CNN
instance segmentation on the COCO dataset as the segmentation network.
Experimental results on the use of different filters are performed. System's
output results show that this novel approach can create satisfying artistic
images with fast operation and simple interface.

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The categories of this paper are: physics
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int
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The categories of this paper are: eess
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The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: stat
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The categories of this paper are: patt-sol