Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Video Action Understanding   Many believe that the successes of deep learning on image understanding
problems can be replicated in the realm of video understanding. However, due to
the scale and temporal nature of video, the span of video understanding
problems and the set of proposed deep learning solutions is arguably wider and
more diverse than those of their 2D image siblings. Finding, identifying, and
predicting actions are a few of the most salient tasks in this emerging and
rapidly evolving field. With a pedagogical emphasis, this tutorial introduces
and systematizes fundamental topics, basic concepts, and notable examples in
supervised video action understanding. Specifically, we clarify a taxonomy of
action problems, catalog and highlight video datasets, describe common video
data preparation methods, present the building blocks of state-of-the art deep
learning model architectures, and formalize domain-specific metrics to baseline
proposed solutions. This tutorial is intended to be accessible to a general
computer science audience and assumes a conceptual understanding of supervised
learning.

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The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
7
The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: ao-sci
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The categories of this paper are: nlin
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The categories of this paper are: solv-int
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The categories of this paper are: econ
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
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The categories of this paper are: alg-geom