Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hierarchical Label Inference for Video Classification   Videos are a rich source of high-dimensional structured data, with a wide
range of interacting components at varying levels of granularity. In order to
improve understanding of unconstrained internet videos, it is important to
consider the role of labels at separate levels of abstraction. In this paper,
we consider the use of the Bidirectional Inference Neural Network (BINN) for
performing graph-based inference in label space for the task of video
classification. We take advantage of the inherent hierarchy between labels at
increasing granularity. The BINN is evaluated on the first and second release
of the YouTube-8M large scale multilabel video dataset. Our results demonstrate
the effectiveness of BINN, achieving significant improvements against baseline
models.

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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: funct-an
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-th
2
The categories of this paper are: math
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The categories of this paper are: hep-ex
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The categories of this paper are: comp-gas