Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Encoding Video and Label Priors for Multi-label Video Classification on
  YouTube-8M dataset   YouTube-8M is the largest video dataset for multi-label video classification.
In order to tackle the multi-label classification on this challenging dataset,
it is necessary to solve several issues such as temporal modeling of videos,
label imbalances, and correlations between labels. We develop a deep neural
network model, which consists of four components: the frame encoder, the
classification layer, the label processing layer, and the loss function. We
introduce our newly proposed methods and discusses how existing models operate
in the YouTube-8M Classification Task, what insights they have, and why they
succeed (or fail) to achieve good performance. Most of the models we proposed
are very high compared to the baseline models, and the ensemble of the models
we used is 8th in the Kaggle Competition.

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The categories of this paper are: supr-con
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The categories of this paper are: comp-gas
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga