A Case Study of Deep Learning-Based Multi-Modal Methods for Labeling the Presence of Questionable Content in Movie Trailers

Mahsa Shafaei, Christos Smailis, Ioannis Kakadiaris, Thamar Solorio


Abstract
In this work, we explore different approaches to combine modalities for the problem of automated age-suitability rating of movie trailers. First, we introduce a new dataset containing videos of movie trailers in English downloaded from IMDB and YouTube, along with their corresponding age-suitability rating labels. Secondly, we propose a multi-modal deep learning pipeline addressing the movie trailer age suitability rating problem. This is the first attempt to combine video, audio, and speech information for this problem, and our experimental results show that multi-modal approaches significantly outperform the best mono and bimodal models in this task.
Anthology ID:
2021.ranlp-1.146
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
Month:
September
Year:
2021
Address:
Held Online
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1297–1307
Language:
URL:
https://aclanthology.org/2021.ranlp-1.146
DOI:
Bibkey:
Cite (ACL):
Mahsa Shafaei, Christos Smailis, Ioannis Kakadiaris, and Thamar Solorio. 2021. A Case Study of Deep Learning-Based Multi-Modal Methods for Labeling the Presence of Questionable Content in Movie Trailers. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 1297–1307, Held Online. INCOMA Ltd..
Cite (Informal):
A Case Study of Deep Learning-Based Multi-Modal Methods for Labeling the Presence of Questionable Content in Movie Trailers (Shafaei et al., RANLP 2021)
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PDF:
https://preview.aclanthology.org/update-css-js/2021.ranlp-1.146.pdf