Conference Proceedings


Alan F. Smeaton
Tomas E. Ward
Feiyan Hu
Enric Moreu
David Azcona



state of the art transfer learning video ensemble media challenge digital content machine learning

Predicting media memorability using ensemble models (2020)

Abstract Memorability, defined as the quality of being worth remembering, is a pressing issue in media as we struggle to organize and retrieve digital content and make it more useful in our daily lives. The Predicting Media Memorability task in MediaEval 2019 tackles this problem by creating a challenge to automatically predict memorability scores building on the work developed in 2018. Our team ensembled transfer learning approaches with video captions using embeddings and our own pre-computed features which outperformed Medieval 2018’s state-of-the-art architectures.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: INSIGHT Centre for Data Analytics
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning

Full list of authors on original publication

Alan F. Smeaton, Tomas E. Ward, Feiyan Hu, Enric Moreu, David Azcona

Experts in our system

Alan F. Smeaton
Dublin City University
Total Publications: 492
Tomas Ward
Maynooth University
Total Publications: 189
Feiyan Hu
Dublin City University
Total Publications: 16
David Azcona
Dublin City University
Total Publications: 14