Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Qiniu Submission to ActivityNet Challenge 2018   In this paper, we introduce our submissions for the tasks of trimmed activity
recognition (Kinetics) and trimmed event recognition (Moments in Time) for
Activitynet Challenge 2018. In the two tasks, non-local neural networks and
temporal segment networks are implemented as our base models. Multi-modal cues
such as RGB image, optical flow and acoustic signal have also been used in our
method. We also propose new non-local-based models for further improvement on
the recognition accuracy. The final submissions after ensembling the models
achieve 83.5% top-1 accuracy and 96.8% top-5 accuracy on the Kinetics
validation set, 35.81% top-1 accuracy and 62.59% top-5 accuracy on the MIT
validation set.

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The categories of this paper are: eess
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: q-alg
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The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
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The categories of this paper are: astro-ph
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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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: nucl-th