Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semantic-Aware Pretraining for Dense Video Captioning   This report describes the details of our approach for the event
dense-captioning task in ActivityNet Challenge 2021. We present a
semantic-aware pretraining method for dense video captioning, which empowers
the learned features to recognize high-level semantic concepts. Diverse video
features of different modalities are fed into an event captioning module to
generate accurate and meaningful sentences. Our final ensemble model achieves a
10.00 METEOR score on the test set.

20
The categories of this paper are: acc-phys
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The categories of this paper are: eess
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
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The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn