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 #52 – Achieving Data-efficient Neural Networks with Hybrid Concept-based Models

February 15, 2024

Latest news

New principal investigators at Visual Intelligence

February 15, 2024

Visual Intelligence congratulates associate professors Elisabeth Wetzer, Ali Ramezani-Kebrya, and Kristoffer Wickstrøm with their recently promoted roles as principal investigators at the research centre.

Two Visual Intelligence papers accepted for prestigious AI conference

February 9, 2024

New information theories and divergences by Visual Intelligence have been developed and accepted in the prestigious International Conference on Learning Representations (ICLR) 2024. ICLR has an acceptance rate of approximately 30 percent.

When:
March 14, 2024
,
14:00
March 14, 2024
,
16:00
@
Auditorium 1.023, Teknologibygget, UiT.

Pitch day at UiT for external master’s projects in computer science and machine learning

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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