8 citations · 28 across the 8 of their papers we have counts for
8 papers
3R-INN: How to be climate friendly while consuming/delivering videos?
Zoubida Ameur, Claire-Hélène Demarty, Daniel Menard +1
The consumption of a video requires a considerable amount of energy during the various stages of its life-cycle. With a billion hours of video consumed daily, this contributes sign…
Overview of The MediaEval 2022 Predicting Video Memorability Task
Lorin Sweeney, Mihai Gabriel Constantin, Claire-Hélène Demarty +8
This paper describes the 5th edition of the Predicting Video Memorability Task as part of MediaEval2022. This year we have reorganised and simplified the task in order to lubricate…
Deep-based Film Grain Removal and Synthesis
Zoubida Ameur, Wassim Hamidouche, Edouard François +3
In this paper, deep learning-based techniques for film grain removal and synthesis that can be applied in video coding are proposed. Film grain is inherent in analog film content b…
Overview of The MediaEval 2021 Predicting Media Memorability Task
Rukiye Savran Kiziltepe, Mihai Gabriel Constantin, Claire-Helene Demarty +8
This paper describes the MediaEval 2021 Predicting Media Memorability}task, which is in its 4th edition this year, as the prediction of short-term and long-term video memorability…
An Annotated Video Dataset for Computing Video Memorability
Rukiye Savran Kiziltepe, Lorin Sweeney, Mihai Gabriel Constantin +6
Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1,275 users manually annotated each video multiple times to indic…
Overview of MediaEval 2020 Predicting Media Memorability Task: What Makes a Video Memorable?
Alba García Seco De Herrera, Rukiye Savran Kiziltepe, Jon Chamberlain +5
This paper describes the MediaEval 2020 \textit{Predicting Media Memorability} task. After first being proposed at MediaEval 2018, the Predicting Media Memorability task is in its…
VideoMem: Constructing, Analyzing, Predicting Short-term and Long-term Video Memorability
Romain Cohendet, Claire-Hélène Demarty, Ngoc Q. K. Duong +1
Humans share a strong tendency to memorize/forget some of the visual information they encounter. This paper focuses on providing computational models for the prediction of the intr…
MediaEval 2018: Predicting Media Memorability Task
Romain Cohendet, Claire-Hélène Demarty, Ngoc Duong +4
In this paper, we present the Predicting Media Memorability task, which is proposed as part of the MediaEval 2018 Benchmarking Initiative for Multimedia Evaluation. Participants ar…