Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Merging Tasks for Video Panoptic Segmentation   In this paper, the task of video panoptic segmentation is studied and two
different methods to solve the task will be proposed. Video panoptic
segmentation (VPS) is a recently introduced computer vision task that requires
classifying and tracking every pixel in a given video. The nature of this task
makes the cost of annotating datasets for it prohibiting. To understand video
panoptic segmentation, first, earlier introduced constituent tasks that focus
on semantics and tracking separately will be researched. Thereafter, two
data-driven approaches which do not require training on a tailored VPS dataset
will be selected to solve it. The first approach will show how a model for
video panoptic segmentation can be built by heuristically fusing the outputs of
a pre-trained semantic segmentation model and a pre-trained multi-object
tracking model. This can be desired if one wants to easily extend the
capabilities of either model. The second approach will counter some of the
shortcomings of the first approach by building on top of a shared neural
network backbone with task-specific heads. This network is designed for
panoptic segmentation and will be extended by a mask propagation module to link
instance masks across time, yielding the video panoptic segmentation format.

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The categories of this paper are: cond-mat
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The categories of this paper are: math
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: acc-phys
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The categories of this paper are: comp-gas
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The categories of this paper are: physics
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: atom-ph
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The categories of this paper are: hep-lat
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The categories of this paper are: chem-ph
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The categories of this paper are: mtrl-th
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The categories of this paper are: quant-ph