Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PanoNet: Real-time Panoptic Segmentation through Position-Sensitive
  Feature Embedding   We propose a simple, fast, and flexible framework to generate simultaneously
semantic and instance masks for panoptic segmentation. Our method, called
PanoNet, incorporates a clean and natural structure design that tackles the
problem purely as a segmentation task without the time-consuming detection
process. We also introduce position-sensitive embedding for instance grouping
by accounting for both object's appearance and its spatial location. Overall,
PanoNet yields high panoptic quality results of high-resolution Cityscapes
images in real-time, significantly faster than all other methods with
comparable performance. Our approach well satisfies the practical speed and
memory requirement for many applications like autonomous driving and augmented
reality.

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The categories of this paper are: astro-ph
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The categories of this paper are: econ
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The categories of this paper are: nlin
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The categories of this paper are: hep-ex
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The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
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The categories of this paper are: chem-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: plasm-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat