Zobrazeno 1 - 10
of 12
pro vyhledávání: '"Wael Abd-Almageed"'
Autor:
Daniel Moreira, João Phillipe Cardenuto, Ruiting Shao, Sriram Baireddy, Davide Cozzolino, Diego Gragnaniello, Wael Abd-Almageed, Paolo Bestagini, Stefano Tubaro, Anderson Rocha, Walter Scheirer, Luisa Verdoliva, Edward Delp
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-15 (2022)
Abstract A great deal of the images found in scientific publications are retouched, reused, or composed to enhance the quality of the presentation. In most instances, these edits are benign and help the reader better understand the material in a pape
Externí odkaz:
https://doaj.org/article/6ec4eb7c878243b689396cda4c2084be
Publikováno v:
PLoS ONE, Vol 8, Iss 11, p e78624 (2013)
The high tumor heterogeneity makes it very challenging to identify key tumorigenic pathways as therapeutic targets. The integration of multiple omics data is a promising approach to identify driving regulatory networks in patient subgroups. Here, we
Externí odkaz:
https://doaj.org/article/2fe7b371a4ce4b82af1ef8072158c73e
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031250620
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::737c03b58cdb9fda33e7aac0f27ad35b
https://doi.org/10.1007/978-3-031-25063-7_5
https://doi.org/10.1007/978-3-031-25063-7_5
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031250842
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d3cada0a1199bc58d9e4ed3fd57f718f
https://doi.org/10.1007/978-3-031-25085-9_22
https://doi.org/10.1007/978-3-031-25085-9_22
Autor:
Gourav Datta, Souvik Kundu, Zihan Yin, Joe Mathai, Zeyu Liu, Zixu Wang, Mulin Tian, Shunlin Lu, Ravi Teja Lakkireddy, Andrew Schmidt, Wael Abd-Almageed, Ajey Jacob, Akhilesh Jaiswal, Peter Beerel
Publikováno v:
2022 IFIP/IEEE 30th International Conference on Very Large Scale Integration (VLSI-SoC).
A large number of deep neural network based techniques have been developed to address the challenging problem of face presentation attack detection (PAD). Whereas such techniques' focus has been on improving PAD performance in terms of classification
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd1cb08ef7c4af9ca2ef127bbd51153d
http://arxiv.org/abs/2111.04862
http://arxiv.org/abs/2111.04862
Autor:
Jiazhi Li, Wael Abd-Almageed
As equality issues in the use of face recognition have garnered a lot of attention lately, greater efforts have been made to debiased deep learning models to improve fairness to minorities. However, there is still no clear definition nor sufficient a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9ddd44c6094ac78cc04c77ab96e69ad2
http://arxiv.org/abs/2111.04673
http://arxiv.org/abs/2111.04673
Advances in deep learning, combined with availability of large datasets, have led to impressive improvements in face presentation attack detection research. However, state-of-the-art face antispoofing systems are still vulnerable to novel types of at
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7a985738fa167c1def4810933243205
http://arxiv.org/abs/2108.12081
http://arxiv.org/abs/2108.12081
Autor:
Daniel Moreira, João Phillipe Cardenuto, Ruiting Shao, Sriram Baireddy, Davide Cozzolino, Diego Gragnaniello, Wael Abd-Almageed, Paolo Bestagini, Stefano Tubaro, Anderson Rocha, Walter Scheirer, Luisa Verdoliva, Edward Delp
Publikováno v:
Scientific reports. 12(1)
A great deal of the images found in scientific publications are retouched, reused, or composed to enhance the quality of the presentation. In most instances, these edits are benign and help the reader better understand the material in a paper. Howeve
Few-shot Learning has been studied to mimic human visual capabilities and learn effective models without the need of exhaustive human annotation. Even though the idea of meta-learning for adaptation has dominated the few-shot learning methods, how to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71a73db2c4ecf3fa10cf66fc2a6a58df