Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting Media Memorability: Comparing Visual, Textual and Auditory
  Features   This paper describes our approach to the Predicting Media Memorability task
in MediaEval 2021, which aims to address the question of media memorability by
setting the task of automatically predicting video memorability. This year we
tackle the task from a comparative standpoint, looking to gain deeper insights
into each of three explored modalities, and using our results from last year's
submission (2020) as a point of reference. Our best performing short-term
memorability model (0.132) tested on the TRECVid2019 dataset -- just like last
year -- was a frame based CNN that was not trained on any TRECVid data, and our
best short-term memorability model (0.524) tested on the Memento10k dataset,
was a Bayesian Ride Regressor fit with DenseNet121 visual features.

8
The categories of this paper are: hep-ex
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
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The categories of this paper are: supr-con
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
14
The categories of this paper are: math-ph
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The categories of this paper are: q-fin