Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Aggregating Frame-level Features for Large-Scale Video Classification   This paper introduces the system we developed for the Google Cloud &
YouTube-8M Video Understanding Challenge, which can be considered as a
multi-label classification problem defined on top of the large scale YouTube-8M
Dataset. We employ a large set of techniques to aggregate the provided
frame-level feature representations and generate video-level predictions,
including several variants of recurrent neural networks (RNN) and generalized
VLAD. We also adopt several fusion strategies to explore the complementarity
among the models. In terms of the official metric GAP@20 (global average
precision at 20), our best fusion model attains 0.84198 on the public 50\% of
test data and 0.84193 on the private 50\% of test data, ranking 4th out of 650
teams worldwide in the competition.

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The categories of this paper are: eess
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
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The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
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The categories of this paper are: stat
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The categories of this paper are: q-alg
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The categories of this paper are: hep-th
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The categories of this paper are: alg-geom
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The categories of this paper are: chem-ph
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The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an