Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Tudor-Alexandru Ileni"'
Autor:
Delia Elena Székely, Darius Dobra, Alexandra Elena Dobre, Victor Domşa, Bogdan Gabriel Drăghici, Tudor-Alexandru Ileni, Robert Konievic, Szilárd Molnár, Paul Sucala, Elena Zah, Adrian Sergiu Darabant, Attila Sándor, Levente Tamás
Publikováno v:
Heliyon, Vol 10, Iss 14, Pp e34017- (2024)
Vine disease detection is considered one of the most crucial components in precision viticulture. It serves as an input for several further modules, including mapping, automatic treatment, and spraying devices. In the last few years, several approach
Externí odkaz:
https://doaj.org/article/f26b509ed3ef4d7db7577e864ac37e1e
Publikováno v:
Mathematics, Vol 10, Iss 22, p 4285 (2022)
Large-scale deep learning models have achieved impressive results on a variety of tasks; however, their deployment on edge or mobile devices is still a challenge due to the limited available memory and computational capability. Knowledge distillation
Externí odkaz:
https://doaj.org/article/5a308cbcac93469790df7e2d5f125d17
Publikováno v:
Complex & Intelligent Systems. 9:2177-2188
Accessibility on edge devices and the trade-off between latency and accuracy is an area of interest in deploying deep learning models. This paper explores a Mixture of Experts system, namely, DynK-Hydra, which allows training of an ensemble formed of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a629a4649ad0262e03edce35afdc667c
https://doi.org/10.1007/s40747-022-00897-1
https://doi.org/10.1007/s40747-022-00897-1
Publikováno v:
Computer Vision and Image Understanding. 228:103632
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ed534b7ab722f84f91e3971509f9c226
https://doi.org/10.1016/j.cviu.2023.103632
https://doi.org/10.1016/j.cviu.2023.103632
Publikováno v:
SoftCOM
3D Face Reconstruction is a complex problem in Computer Vision. Until recently the existing methods were based on multiple image captures and solving complex dense correspondences between different face poses. Recent methods are based on volumetric C
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::59a8622420a2bb727d4c13a3cd2b6dd1
https://doi.org/10.23919/softcom50211.2020.9238250
https://doi.org/10.23919/softcom50211.2020.9238250
Publikováno v:
SoftCOM
In this paper we present a 3D facial reconstruction algorithm for facial shape classification. We propose a reconstruction method that preserves facial ratios independent on the captured poses allowing thus a precise facial shape classification/deter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::172bb3ccd0b8e0d2fa4321629cbed762
https://doi.org/10.23919/softcom.2019.8903745
https://doi.org/10.23919/softcom.2019.8903745
Publikováno v:
VISIGRAPP (4: VISAPP)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2bfd6b8523cc39cd0acb6c5e0d2207fa
https://doi.org/10.5220/0007250500590066
https://doi.org/10.5220/0007250500590066
Publikováno v:
Sustainability
Volume 12
Issue 22
Volume 12
Issue 22
Machine learning is a branch of artificial intelligence that has gained a lot of traction in the last years due to advances in deep neural networks. These algorithms can be used to process large quantities of data, which would be impossible to handle
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8337b1e1c8367aa68ea77bd1dbc99ed
https://doi.org/10.3390/su12229707
https://doi.org/10.3390/su12229707
Publikováno v:
Advanced Concepts for Intelligent Vision Systems ISBN: 9783030014483
ACIVS
ACIVS
In this paper we tackle the problem of hair analysis in unconstrained images. We propose a fully convolutional, multi-task neural network to segment the image pixels into hair, face and background classes. The network also decides if the person is ba
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::13c3d3ff95e58c3f7cbe5c0431e4149a
https://doi.org/10.1007/978-3-030-01449-0_37
https://doi.org/10.1007/978-3-030-01449-0_37
