Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: UC Merced Submission to the ActivityNet Challenge 2016   This notebook paper describes our system for the untrimmed classification
task in the ActivityNet challenge 2016. We investigate multiple
state-of-the-art approaches for action recognition in long, untrimmed videos.
We exploit hand-crafted motion boundary histogram features as well feature
activations from deep networks such as VGG16, GoogLeNet, and C3D. These
features are separately fed to linear, one-versus-rest support vector machine
classifiers to produce confidence scores for each action class. These
predictions are then fused along with the softmax scores of the recent
ultra-deep ResNet-101 using weighted averaging.

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The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: q-fin
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ph
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The categories of this paper are: q-alg
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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: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: nucl-ex