Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Video Instance Segmentation   In this paper we present a new computer vision task, named video instance
segmentation. The goal of this new task is simultaneous detection, segmentation
and tracking of instances in videos. In words, it is the first time that the
image instance segmentation problem is extended to the video domain. To
facilitate research on this new task, we propose a large-scale benchmark called
YouTube-VIS, which consists of 2883 high-resolution YouTube videos, a
40-category label set and 131k high-quality instance masks. In addition, we
propose a novel algorithm called MaskTrack R-CNN for this task. Our new method
introduces a new tracking branch to Mask R-CNN to jointly perform the
detection, segmentation and tracking tasks simultaneously. Finally, we evaluate
the proposed method and several strong baselines on our new dataset.
Experimental results clearly demonstrate the advantages of the proposed
algorithm and reveal insight for future improvement. We believe the video
instance segmentation task will motivate the community along the line of
research for video understanding.

2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
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The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
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The categories of this paper are: eess
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The categories of this paper are: bayes-an