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pro vyhledávání: '"de Herrera, Alba García Seco"'
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:
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
Image retrieval is a complex task that differs according to the context and the user requirements in any specific field, for example in a medical environment. Search by text is often not possible or optimal and retrieval by the visual content does no
Externí odkaz:
http://arxiv.org/abs/1701.05596
Autor:
Kalpathy-Cramer, Jayashree, de Herrera, Alba García Seco, Demner-Fushman, Dina, Antani, Sameer, Bedrick, Steven, Müller, Henning
Publikováno v:
In Computerized Medical Imaging and Graphics January 2015 39:55-61
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Akademický článek
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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:
de Herrera, Alba G. Seco ORCID: 0000-0002-6509-5325 , Kiziltepe, Rukiye Savran ORCID: 0000-0002-3862-7621 , Chamberlain, Jon ORCID: 0000-0002-6947-8964 , Constantin, Mihai Gabriel ORCID: 0000-0002-2312-6672 , Demarty, Claire-Hélène, Doctor, Faiyaz ORCID: 0000-0002-8412-5489 , Ionescu, Bogdan ORCID: 0000-0003-4112-5769 and Smeaton, Alan F. ORCID: 0000-0003-1028-8389 (2020) Overview of MediaEval 2020 predicting media memorability task: what makes a video memorable? In: MediaEval 2020 Multimedia Benchmark Workshop, 14-15 Dec 2020, Online.
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e64f1e4f499afc815ea2a557aac16c2
Akademický článek
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