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

no code implementations • RANLP 2021 • Mahsa Shafaei, Christos Smailis, Ioannis Kakadiaris, Thamar Solorio

In this work, we explore different approaches to combine modalities for the problem of automated age-suitability rating of movie trailers.

White Paper - Creating a Repository of Objectionable Online Content: Addressing Undesirable Biases and Ethical Considerations

no code implementations • 23 Feb 2021 • Thamar Solorio, Mahsa Shafaei, Christos Smailis, Isabelle Augenstein, Margaret Mitchell, Ingrid Stapf, Ioannis Kakadiaris

This white paper summarizes the authors' structured brainstorming regarding ethical considerations for creating an extensive repository of online content labeled with tags that describe potentially questionable content for young viewers.

A Case Study of Deep Learning Based Multi-Modal Methods for Predicting the Age-Suitability Rating of Movie Trailers

no code implementations • 26 Jan 2021 • Mahsa Shafaei, Christos Smailis, Ioannis A. Kakadiaris, Thamar Solorio

In this work, we explore different approaches to combine modalities for the problem of automated age-suitability rating of movie trailers.

White Paper: Challenges and Considerations for the Creation of a Large Labelled Repository of Online Videos with Questionable Content

no code implementations • 25 Jan 2021 • Thamar Solorio, Mahsa Shafaei, Christos Smailis, Mona Diab, Theodore Giannakopoulos, Heng Ji, Yang Liu, Rada Mihalcea, Smaranda Muresan, Ioannis Kakadiaris

This white paper presents a summary of the discussions regarding critical considerations to develop an extensive repository of online videos annotated with labels indicating questionable content.