no code implementations • 13 Dec 2022 • Lorin Sweeney, Mihai Gabriel Constantin, Claire-Hélène Demarty, Camilo Fosco, Alba G. Seco de Herrera, Sebastian Halder, Graham Healy, Bogdan Ionescu, Ana Matran-Fernandez, Alan F. Smeaton, Mushfika Sultana
This paper describes the 5th edition of the Predicting Video Memorability Task as part of MediaEval2022.
no code implementations • 7 Dec 2022 • Alba García Deco de Herrera, Mihai Gabriel Constantin, Chaire-Hélène Demarty, Camilo Fosco, Sebastian Halder, Graham Healy, Bogdan Ionescu, Ana Matran-Fernandez, Alan F. Smeaton, Mushfika Sultana, Lorin Sweeney
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 time.
Second, they allow us to generate novel "Deepfake Caricatures": transformations of the deepfake that exacerbate artifacts to improve human detection.
no code implementations • 11 Dec 2021 • Rukiye Savran Kiziltepe, Mihai Gabriel Constantin, Claire-Helene Demarty, Graham Healy, Camilo Fosco, Alba Garcia Seco de Herrera, Sebastian Halder, Bogdan Ionescu, Ana Matran-Fernandez, Alan F. Smeaton, Lorin Sweeney
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 remains a challenging task.
An inherent property of real-world videos is the high correlation of information across frames which can translate into redundancy in either temporal or spatial feature maps of the models, or both.
Based on our findings we propose a new mathematical formulation of memorability decay, resulting in a model that is able to produce the first quantitative estimation of how a video decays in memory over time.
This allows our model to perform cognitive tasks such as set abstraction (which general concept is in common among a set of videos?
This paper introduces a Unified Model of Saliency and Importance (UMSI), which learns to predict visual importance in input graphic designs, and saliency in natural images, along with a new dataset and applications.
What jumps out in a single glance of an image is different than what you might notice after closer inspection.