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of 142
pro vyhledávání: '"Herrera, Alba"'
Autor:
Rückert, Johannes, Bloch, Louise, Brüngel, Raphael, Idrissi-Yaghir, Ahmad, Schäfer, Henning, Schmidt, Cynthia S., Koitka, Sven, Pelka, Obioma, Abacha, Asma Ben, de Herrera, Alba G. Seco, Müller, Henning, Horn, Peter A., Nensa, Felix, Friedrich, Christoph M.
Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext version 2 (ROCOv2), a multimodal dat
Externí odkaz:
http://arxiv.org/abs/2405.10004
Contextualised word vectors obtained via pre-trained language models encode a variety of knowledge that has already been exploited in applications. Complementary to these language models are probabilistic topic models that learn thematic patterns fro
Externí odkaz:
http://arxiv.org/abs/2301.04339
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
Autor:
Sweeney, Lorin, Matran-Fernandez, Ana, Halder, Sebastian, de Herrera, Alba G. Seco, Smeaton, Alan, Healy, Graham
The aim of the Memorability-EEG pilot subtask at MediaEval'2021 is to promote interest in the use of neural signals -- either alone or in combination with other data sources -- in the context of predicting video memorability by highlighting the utili
Externí odkaz:
http://arxiv.org/abs/2201.00620
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:
Kiziltepe, Rukiye Savran, Sweeney, Lorin, Constantin, Mihai Gabriel, Doctor, Faiyaz, de Herrera, Alba Garcia Seco, Demarty, Claire-Helene, Healy, Graham, Ionescu, Bogdan, Smeaton, Alan F.
Publikováno v:
Data in Brief, Volume 39, 107671, (2021), ISSN 2352-3409
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 indicate both long-term and short-term memorability of the videos. The anno
Externí odkaz:
http://arxiv.org/abs/2112.02303
Autor:
De Herrera, Alba García Seco, Kiziltepe, Rukiye Savran, Chamberlain, Jon, Constantin, Mihai Gabriel, Demarty, Claire-Hélène, Doctor, Faiyaz, Ionescu, Bogdan, Smeaton, Alan F.
Publikováno v:
MediaEval Multimedia Benchmark Workshop Working Notes, 14-15 December 2020
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 3rd edition this year, as the prediction of short-term and long-term v
Externí odkaz:
http://arxiv.org/abs/2012.15650
Publikováno v:
In Process Safety and Environmental Protection January 2023 169:252-259
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