A Norwegian centre for research-based innovation

Through long-term research in close collaboration between industry, public institutions and prominent research partners, we enable novel innovations, technology transfer, internationalization and researcher training.

Learn More

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.

Did you miss this?

VI Seminar #70: Representation Learning of Visual Features Through Self-Supervision

February 27, 2025

Latest news

Successful Industry Pitch Day at UiT

March 20, 2025

Visual Intelligence and the Digital Innovation Lab invited industry professionals to present ideas for master's projects to computer science and machine learning students at UiT The Arctic University of Norway.

Dagens Næringsliv: Norges eldste fagmiljø innen KI

March 18, 2025

Kunstig intelligens (KI) endrer måten vi løser komplekse problemer på. Ved UiT Norges arktiske universitet leder professor Robert Jenssen Visual Intelligence, et senter for forskningsdrevet innovasjon som utvikler neste generasjons KI-metoder.

When:
June 18, 2025
,
11:00
June 19, 2025
,
13:00
@
UiT The Arctic University of Norway, Campus Breivika

The 2025 Symposium of the Norwegian AI Society will take place from June 18 to June 19 at the Arctic University of Norway in Tromsø. The symposium aims at bringing together researchers and practitioners in the field of Artificial Intelligence from Norway and Scandinavia to present on-going work and discuss the future directions of AI.

twitterfacebookYoutubeGithub logoSign up for the Visual Intelligence newsletter.

Visual Data Diagnosis and Debiasing with Concept Graphs

March 6, 2025

We propose ConBias, a bias diagnosis and debiasing pipeline for visual datasets.

Read More

Modular Superpixel Tokenization in Vision Transformers

March 6, 2025

ViTs partition images into square patches to extract tokenized features. But is this necessarily an optimal way of partitioning images?

Recent publications

Fast TILs—A pipeline for efficient TILs estimation in non-small cell lung cancer

By authors:

Nikita Shvetsov, Anders Sildnes, Masoud Tafavvoghi, Lill-Tove Rasmussen Busund, Stig Manfred Dalen, Kajsa Møllersen, Lars Ailo Bongo, Thomas K. Kilvaer,

Published in:

Journal of Pathology Informatics

on

March 12, 2025

Motion compansated interpolation in echocardiography: A Lie-advection based approach

By authors:

H. N. Mirar, S. R. Snare and A. H. S. Solberg

Published in:

IEEE Transactions on Biomedical Engineering, vol. 72, no. 1, pp. 123-136, Jan. 2025

on

January 16, 2025

From Flexibility to Manipulation: The Slippery Slope of XAI Evaluation

By authors:

Wickstrom, Kristoffer; Höhne, Marina; Hedström, Anna.

Published in:

European Conference on Computer Vision (ECCV) 2024 Workshop: Explainable Computer Vision: Where are We and Where are We Going?, 2024.

on

December 7, 2024

Deep learning-based postoperative glioblastoma segmentation and extent of resection evaluation: Development, external validation, and model comparison

By authors:

Santiago Cepeda, Roberto Romero, Lidia Luque, Daniel García-Pérez, Guillermo Blasco, Luigi Tommaso Luppino, Samuel Kuttner, Olga Esteban-Sinovas, Ignacio Arrese, Ole Solheim, Live Eikenes, Anna Karlberg, Ángel Pérez-Núñez, Olivier Zanier, Carlo Serra, Victor E Staartjes, Andrea Bianconi, Luca Francesco Rossi, Diego Garbossa, Trinidad Escudero, Roberto Hornero, Rosario Sarabia

Published in:

Neuro-Oncology Advances, Volume 6, Issue 1, January-December 2024, vdae199

on

November 16, 2024

Polar mesospheric summer echo (PMSE) multilayer properties during the solar maximum and solar minimum

By authors:

Jozwicki, D., Sharma, P., Huyghebaert, D., and Mann, I.

Published in:

Ann. Geophys., 42, 431–453,

on

November 11, 2024

Better, Not Just More: Data-centric machine learning for Earth observation

By authors:

Roscher, Ribana; Russwurm, Marc; Gevaert, Caroline; Kampffmeyer, Michael Christian; Santos, Jefersson A. Dos; Vakalopoulou, Maria; Hansch, Ronny; Hansen, Stine; Nogueira, Keiller; Prexl, Jonathan; Tuia, Devis

Published in:

EEE Geoscience and Remote Sensing Magazine 2024 s. 1-22

on

October 31, 2024

Research challenges

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.

Read more
Go to our research challenges
Go to our research challenges

Innovation areas

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 more

Our partners

Visual 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.

UiT The Arctic University of Norway logoUiO: University of Oslo logoUniversity hospital of north norway logoHelse nord ikt logoInstitute of marine research logoKongsberg satellite services logoGE Healthcare logoEquinor logoCancer Registry of Norwat logo