Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Juan Leon Alcazar"'
Autor:
Andres Villa, Kumail Alhamoud, Victor Escorcia, Fabian Caba Heilbron, Juan Leon Alcazar, Bernard Ghanem
Continual learning (CL) is under-explored in the video domain. The few existing works contain splits with imbalanced class distributions over the tasks, or study the problem in unsuitable datasets. We introduce vCLIMB, a novel video continual learnin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22d718b7b40606863586bcb72395a88e
http://arxiv.org/abs/2201.09381
http://arxiv.org/abs/2201.09381
Autor:
Mattia Soldan, Alejandro Pardo, Juan Leon Alcazar, Fabian Caba Heilbron, Chen Zhao, Silvio Giancola, Bernard Ghanem
The recent and increasing interest in video-language research has driven the development of large-scale datasets that enable data-intensive machine learning techniques. In comparison, limited effort has been made at assessing the fitness of these dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40520fe8719498d199407300a2c863f2
http://arxiv.org/abs/2112.00431
http://arxiv.org/abs/2112.00431
Autor:
Pablo Arbeláez, Bernard Ghanem, Fabian Caba Heilbron, Joon-Young Lee, Juan Leon Alcazar, Long Mai, Federico Perazzi
Publikováno v:
CVPR Workshops
Humans are arguably one of the most important subjects in video streams, many real-world applications such as video summarization or video editing workflows often require the automatic search and retrieval of a person of interest. Despite tremendous
Video content creation keeps growing at an incredible pace; yet, creating engaging stories remains challenging and requires non-trivial video editing expertise. Many video editing components are astonishingly hard to automate primarily due to the lac
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30ea6d886942eb7adf9b556db2269d45
Autor:
Long Mai, Bernard Ghanem, Joon-Young Lee, Federico Perazzi, Juan Leon Alcazar, Pablo Arbeláez, Fabian Caba
Publikováno v:
CVPR
Current methods for active speak er detection focus on modeling short-term audiovisual information from a single speaker. Although this strategy can be enough for addressing single-speaker scenarios, it prevents accurate detection when the task is to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7eb910b143fbb94a15817cbcdf95dc28
http://arxiv.org/abs/2005.09812
http://arxiv.org/abs/2005.09812