Visual Intelligence publications are financially supported by the Research Council of Norway, through its Centre for Research-based Innovation funding scheme (grant no. 309439), and Consortium Partners. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.
Visual Intelligence publications are financially supported by the Research Council of Norway, through its Centre for Research-based Innovation funding scheme (grant no. 309439), and Consortium Partners. For the purpose of open access, the author has applied a CC BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.
At Visual Intelligence we work across our innovation areas to extract knowledge from large volumes of visual data more efficiently through automatic and intelligent data analysis. The work to address the core research challenges in deep learning: working with limited training data, utilizing context and dependencies, providing explainability, confidence and uncertainty, are important in all the innovation areas.
By authors:
Uteng, Stig; Quevedo, Eduardo; Callico, Gustavo M.; Castaño, Irene; Carretero, Gregorio; Almeida, Pablo; Garcia, Aday; Hernandez, Javier A.; Godtliebsen, Fred.
Published in:
Sensors
on
January 20, 2021
By authors:
Kristoffer Wickstrøm, Michael Kampffmeyer, Robert Jenssen
Published in:
Medical Image Analysis, Volume 60, February 2020, 101619
on
November 14, 2019
By authors:
Ron Levie, Çağkan Yapar, Gitta Kutyniok, Giuseppe Caire
Published in:
ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
on
May 4, 0202