Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Fosco, Camilo"'
Autor:
Fosco, Camilo L.
Fake or manipulated video media (“deepfakes”) pose a clear threat to the integrity of online spaces that rely on video, from social media, to news media, to video conferencing platforms. To the human eye, these computer-generated fake videos are
Externí odkaz:
https://hdl.handle.net/1721.1/154206
The development of technologies for easily and automatically falsifying video has raised practical questions about people's ability to detect false information online. How vulnerable are people to deepfake videos? What technologies can be applied to
Externí odkaz:
http://arxiv.org/abs/2304.04733
Autor:
Sweeney, Lorin, Constantin, Mihai Gabriel, Demarty, Claire-Hélène, Fosco, Camilo, de Herrera, Alba G. Seco, Halder, Sebastian, Healy, Graham, Ionescu, Bogdan, Matran-Fernandez, Ana, Smeaton, Alan F., Sultana, Mushfika
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 a greater depth of inquiry. Similar to last year, two datasets are pr
Externí odkaz:
http://arxiv.org/abs/2212.06516
Autor:
de Herrera, Alba García Deco, Constantin, Mihai Gabriel, Demarty, Chaire-Hélène, Fosco, Camilo, Halder, Sebastian, Healy, Graham, Ionescu, Bogdan, Matran-Fernandez, Ana, Smeaton, Alan F., Sultana, Mushfika, Sweeney, Lorin
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. This has allowed us to compare the performance of many memorabilit
Externí odkaz:
http://arxiv.org/abs/2212.03955
Deepfakes pose a serious threat to digital well-being by fueling misinformation. As deepfakes get harder to recognize with the naked eye, human users become increasingly reliant on deepfake detection models to decide if a video is real or fake. Curre
Externí odkaz:
http://arxiv.org/abs/2206.00535
Autor:
Kiziltepe, Rukiye Savran, Constantin, Mihai Gabriel, Demarty, Claire-Helene, Healy, Graham, Fosco, Camilo, de Herrera, Alba Garcia Seco, Halder, Sebastian, Ionescu, Bogdan, Matran-Fernandez, Ana, Smeaton, Alan F., Sweeney, Lorin
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. In 2021, two datasets of videos are used:
Externí odkaz:
http://arxiv.org/abs/2112.05982
Autor:
Pan, Bowen, Panda, Rameswar, Fosco, Camilo, Lin, Chung-Ching, Andonian, Alex, Meng, Yue, Saenko, Kate, Oliva, Aude, Feris, Rogerio
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 property of real-world videos is the high correlation of information ac
Externí odkaz:
http://arxiv.org/abs/2102.07887
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 model of human visual event memory and how those memories decay over
Externí odkaz:
http://arxiv.org/abs/2009.02568
Autor:
Andonian, Alex, Fosco, Camilo, Monfort, Mathew, Lee, Allen, Feris, Rogerio, Vondrick, Carl, Oliva, Aude
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 relational set abstractions on videos, inspired by human learning. We
Externí odkaz:
http://arxiv.org/abs/2008.05596
Autor:
Fosco, Camilo, Casser, Vincent, Bedi, Amish Kumar, O'Donovan, Peter, Hertzmann, Aaron, Bylinskii, Zoya
Publikováno v:
Proceedings of UIST 2020
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. Previous methods for predicting sa
Externí odkaz:
http://arxiv.org/abs/2008.02912