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.

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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.

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VI Seminar #54 – Generative AI for high quality 2D and 3D echo images

March 14, 2024

Latest news

Successful PhD defense by Srishti Gautam

March 15, 2024

We congratulate Srishti Gautam for successfully defending her PhD thesis and achieving the PhD degree in Science at UiT The Arctic University of Norway.

- UiT har en viktig rolle i utviklingen av KI i nord

March 12, 2024

Det sier førsteamanuensis Kristoffer Wickstrøm. På NHO Arktis sin årskonferanse i Alta delte han innsikt om KI-potensialet og argumenterte for hvorfor nordnorske bedrifter bør investere i denne teknologien. (Norwegian article).

When:
September 24, 2024
,
9:00
September 25, 2024
,
16:00
@
Quality Airport Hotel Gardermoen

Annual Visual Intelligence workshop to strengthened technology transfer, as well as knowledge transfer within the Visual intelligence consortium.

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On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering

December 19, 2023

We propose DeepMVC – a unified framework which includes many recent methods as instances.

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Merging clustering into deep supervised neural network

June 8, 2023

Introducing the SuperCM technique to significantly improve classification results across various types of image data.

Recent publications

A Contextually Supported Abnormality Detector for Maritime Trajectories

By authors:

Olesen, Kristoffer Vinther; Boubekki, Ahcene; Kampffmeyer, Michael Christian; Jenssen, Robert; Christensen, Anders Nymark; Hørlück, Sune; Clemmensen, Line H. A

Published in:

Journal of Marine Science and Engineering (JMSE) 2023 ;Volum 11.(11)

on

October 31, 2023

View it like a radiologist: Shifted windows for deep learning augmentation of CT images

By authors:

Østmo, Eirik Agnalt; Wickstrøm, Kristoffer; Radiya, Keyur; Kampffmeyer, Michael; Jenssen, Robert.

Published in:

2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP), Rome, Italy, 2023, pp. 1-6

on

October 23, 2023

On Measures of Uncertainty in Classification

By authors:

Chlaily, Saloua; Ratha, Debanshu; Lozou, Pigi; Marinoni, Andrea

Published in:

IEEE Transactions on Signal Processing 2023 ;Volum 71. s.3710-3725

on

October 12, 2023

SelfGraphVQA: A Self-Supervised Graph Neural Network for Scene-based Question Answering

By authors:

Bruno Souza; Marius Aasan; Helio Pedrini; Adıń Ramıŕez Rivera

Published in:

2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France, 2023, pp. 4642-4647

on

October 2, 2023

Selective Imputation for Multivariate Time Series Datasets with Missing Values.

By authors:

Blazquez-Garcia, Ane; Wickstrøm, Kristoffer Knutsen; Yu, Shujian; Mikalsen, Karl Øyvind; Boubekki, Ahcene; Conde, Angel; Mori, Usue; Jenssen, Robert; Lozano, Jose A.

Published in:

Transactions on Knowledge and Data Engineering, vol. 35, no. 9, pp. 9490-9501

on

September 1, 2023

Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation

By authors:

Tomasetti, Luca; Hansen, Stine; Khanmohammadi, Mahdieh; Engan, Kjersti; Høllesli, Liv Jorunn; Kurz, Kathinka Dæhli; Kampffmeyer, Michael Christian

Published in:

2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI), Cartagena, Colombia, 2023, pp

on

September 1, 2023

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.

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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.

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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 logoNorwegian Computing Centre logoUniversity hospital of north norway logoHelse nord ikt logoInstitute of marine research logoKongsberg satellite services logoGE Healthcare logoEquinor logoCancer Registry of Norwat logo