We research the next generation of deep learning methodology for visual data and produce solutions for our consortium partners across innovation areas in medicine and health, marine science, energy, and earth observation.
June 19, 2025
A new study shows how deep learning can achieve human-level performance in estimating uncertainty when classifying foraminifera (news story in EurekAlert.org)
Congratulations to Iver Martinsen and Durgesh Kumar Singh, who successfully defended their PhD theses at UiT The Arctic University of Norway on August 20th and 21st respectively.
This workshop hosts leading researchers to examine how their theoretical foundations expose a paradox between statistical compression and semantic meaning, how emergent phenomena challenge conventional assumptions, and how evaluation practices continue to shape trustworthy foundation models.
New study shows how deep learning can achieve human-level performance in estimating uncertainty when classifying foraminifera.
We propose ConBias, a bias diagnosis and debiasing pipeline for visual datasets.
By authors:
Iver Martinsen, Steffen Aagaard Sørensen, Samuel Ortega, Fred Godtliebsen, Miguel Tejedor, Eirik Myrvoll-Nilsen
Published in:
Artificial Intelligence in Geosciences
on
July 16, 2025
By authors:
Changkyu Choi, Arangan Subramaniam, Nils Olav Handegard, Ali Ramezani-Kebrya and Robert Jenssen
Published in:
Proceedings of the Symposium of the Norwegian AI Society 2025, CEUR Workshop Proceedings ( ISSN 1613-0073)
on
June 17, 2025
By authors:
Marius Aasan, Adín Ramírez Rivera
Published in:
Proceedings of the Symposium of the Norwegian AI Society 2025, CEUR Workshop Proceedings ( ISSN 1613-0073)
on
June 17, 2025
By authors:
Suaiba A. Salahuddin, Elisabeth Wetzer, Kristoffer Wickstrøm, Solveig Thrun, Michael Kampffmeyer and Robert Jenssen
Published in:
Lecture Notes in Computer Science (LNCS) 2025 ;Volum 15726.
on
June 16, 2025
By authors:
Hyeongji Kim, Changkyu Choi, Michael Christian Kampffmeyer, Terje Berge, Pekka Parviainen, Ketil Malde
Published in:
Lecture Notes in Computer Science (LNCS) 2025
on
May 12, 2025
By authors:
Zhiyuan Wu, Changkyu Choi, Volkan Cevher, Ali Ramezani-Kebrya
Published in:
International Conference on Learning Representations 2025
on
April 29, 2025
Visual Intelligence address the research challenges of deep learning and computer vision that limit our user partners in utilizing their complex visual data in their applications.
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We contribute to reliable use of AI to detect heart disease, monitor the environment and potential natural disasters as well as detecting natural resources. Read more about our work in the different innovation areas.
Read moreVisual Intelligence is a consortium headed by UiT The Arctic University of Norway with research partners at the University of Oslo and the Norwegian Computing Center. Together with our consortium of high-profile user partners, we create cutting-edge solutions that will be implemented in the applications of the user partners.
