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:
- 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)
- PDF:
- https://preview.aclanthology.org/starsem-semeval-split/2021.ranlp-1.146.pdf