7 citations · 21 across the 6 of their papers we have counts for
6 papers
Deepfake Sentry: Harnessing Ensemble Intelligence for Resilient Detection and Generalisation
Liviu-Daniel Ştefan, Dan-Cristian Stanciu, Mihai Dogariu +3
Recent advancements in Generative Adversarial Networks (GANs) have enabled photorealistic image generation with high quality. However, the malicious use of such generated media has…
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…
Experiences from the MediaEval Predicting Media Memorability Task
Alba García Deco de Herrera, Mihai Gabriel Constantin, Chaire-Hélène Demarty +8
The Predicting Media Memorability task in the MediaEval evaluation campaign has been running annually since 2018 and several different tasks and data sets have been used in this ti…
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…