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
Congratulations to Rwiddhi Chakraborty for successfully defending his PhD thesis and achieving the PhD degree in Science at UiT The Arctic University of Norway on August 27th 2025.
Det sa digitaliseringsminister Karianne Tung (Ap) da hun besøkte UiT Norges arktiske universitet i Tromsø for å lære mer om utdanning og toppforskning på kunstig intelligens ved universitetet (Norwegian news article at uit.no)
Annual Visual Intelligence workshop to strengthened technology transfer, as well as knowledge transfer within the Visual intelligence consortium.
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:
Martine Hjelkrem-Tan, Marius Aasan, Gabriel Y. Arteaga, and Adín Ramírez Rivera
Published in:
Workshop on Efficient Computing under Limited Resources: Visual Computing (ICCV 2025), Oct 19 – 23th, 2025, Honolulu, Hawai'i
on
October 19, 2025
By authors:
Vilde Schulerud Bøe, Andreas Kleppe, Sebastian Foersch, Daniel-Christoph Wagner, Lill-Tove Rasmussen Busund, Adín Ramírez Rivera
Published in:
MICCAI Workshop on Computational Pathology with Multimodal Data (COMPAYL), DAEJEON, South Korea, 2025
on
September 27, 2025
By authors:
Zijun Sun, Solveig Thrun and Michael Kampffmeyer
Published in:
MICCAI 2025
on
September 17, 2025
By authors:
Hyeongji Kim, Stine Hansen, Michael Kampffmeyer
Published in:
MICCAI 2025
on
September 17, 2025
By authors:
Solveig Thrun, Stine Hansen, Zijun Sun, Nele Blum, Suaiba A. Salahuddin, Kristoffer Wickstrøm, Elisabeth Wetzer, Robert Jenssen, Maik Stille, Michael Kampffmeyer
Published in:
INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION (MICCAI) 2025
on
September 17, 2025
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
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.
