5 citations · 21 across the 8 of their papers we have counts for
10 papers
Artifact magnification on deepfake videos increases human detection and subjective confidence
Emilie Josephs, Camilo Fosco, Aude Oliva
The development of technologies for easily and automatically falsifying video has raised practical questions about people's ability to detect false information online. How vulnerab…
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…
Deepfake Caricatures: Amplifying attention to artifacts increases deepfake detection by humans and machines
Camilo Fosco, Emilie Josephs, Alex Andonian +1
Deepfakes can fuel online misinformation. As deepfakes get harder to recognize with the naked eye, human users become more reliant on deepfake detection models to help them decide…
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…
VA-RED: Video Adaptive Redundancy Reduction
Bowen Pan, Rameswar Panda, Camilo Fosco +6
Performing inference on deep learning models for videos remains a challenge due to the large amount of computational resources required to achieve robust recognition. An inherent p…
Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability
Anelise Newman, Camilo Fosco, Vincent Casser +3
A key capability of an intelligent system is deciding when events from past experience must be remembered and when they can be forgotten. Towards this goal, we develop a predictive…
We Have So Much In Common: Modeling Semantic Relational Set Abstractions in Videos
Alex Andonian, Camilo Fosco, Mathew Monfort +4
Identifying common patterns among events is a key ability in human and machine perception, as it underlies intelligent decision making. We propose an approach for learning semantic…
Predicting Visual Importance Across Graphic Design Types
Camilo Fosco, Vincent Casser, Amish Kumar Bedi +3
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 w…
TurkEyes: A Web-Based Toolbox for Crowdsourcing Attention Data
Anelise Newman, Barry McNamara, Camilo Fosco +5
Eye movements provide insight into what parts of an image a viewer finds most salient, interesting, or relevant to the task at hand. Unfortunately, eye tracking data, a commonly-us…