Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ridiculously Fast Shot Boundary Detection with Fully Convolutional
  Neural Networks   Shot boundary detection (SBD) is an important component of many video
analysis tasks, such as action recognition, video indexing, summarization and
editing. Previous work typically used a combination of low-level features like
color histograms, in conjunction with simple models such as SVMs. Instead, we
propose to learn shot detection end-to-end, from pixels to final shot
boundaries. For training such a model, we rely on our insight that all shot
boundaries are generated. Thus, we create a dataset with one million frames and
automatically generated transitions such as cuts, dissolves and fades. In order
to efficiently analyze hours of videos, we propose a Convolutional Neural
Network (CNN) which is fully convolutional in time, thus allowing to use a
large temporal context without the need to repeatedly processing frames. With
this architecture our method obtains state-of-the-art results while running at
an unprecedented speed of more than 120x real-time.

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The categories of this paper are: mtrl-th
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The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
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The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
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The categories of this paper are: q-fin
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The categories of this paper are: quant-ph
3
The categories of this paper are: physics
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The categories of this paper are: atom-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-ex
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The categories of this paper are: q-alg