Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-Class Relevance Learning for Temporal Concept Localization   We present a novel Cross-Class Relevance Learning approach for the task of
temporal concept localization. Most localization architectures rely on feature
extraction layers followed by a classification layer which outputs class
probabilities for each segment. However, in many real-world applications
classes can exhibit complex relationships that are difficult to model with this
architecture. In contrast, we propose to incorporate target class and
class-related features as input, and learn a pairwise binary model to predict
general segment to class relevance. This facilitates learning of shared
information between classes, and allows for arbitrary class-specific feature
engineering. We apply this approach to the 3rd YouTube-8M Video Understanding
Challenge together with other leading models, and achieve first place out of
over 280 teams. In this paper we describe our approach and show some empirical
results.

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The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
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The categories of this paper are: astro-ph
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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: q-bio
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The categories of this paper are: comp-gas
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The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: cond-mat
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The categories of this paper are: adap-org
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The categories of this paper are: alg-geom
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The categories of this paper are: econ
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The categories of this paper are: supr-con
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The categories of this paper are: funct-an