Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Visual Recognition with Deep Neural Networks: A Survey on
  Recent Advances and New Directions   Visual recognition is currently one of the most important and active research
areas in computer vision, pattern recognition, and even the general field of
artificial intelligence. It has great fundamental importance and strong
industrial needs. Deep neural networks (DNNs) have largely boosted their
performances on many concrete tasks, with the help of large amounts of training
data and new powerful computation resources. Though recognition accuracy is
usually the first concern for new progresses, efficiency is actually rather
important and sometimes critical for both academic research and industrial
applications. Moreover, insightful views on the opportunities and challenges of
efficiency are also highly required for the entire community. While general
surveys on the efficiency issue of DNNs have been done from various
perspectives, as far as we are aware, scarcely any of them focused on visual
recognition systematically, and thus it is unclear which progresses are
applicable to it and what else should be concerned. In this paper, we present
the review of the recent advances with our suggestions on the new possible
directions towards improving the efficiency of DNN-related visual recognition
approaches. We investigate not only from the model but also the data point of
view (which is not the case in existing surveys), and focus on three most
studied data types (images, videos and points). This paper attempts to provide
a systematic summary via a comprehensive survey which can serve as a valuable
reference and inspire both researchers and practitioners who work on visual
recognition problems.

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The categories of this paper are: funct-an
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The categories of this paper are: eess
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
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The categories of this paper are: q-fin
2
The categories of this paper are: math
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The categories of this paper are: physics
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The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: nlin