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 #63 - Finding NEM-U: Explaining unsupervised representation learning through neural network generated explanation masks

October 24, 2024

When:
November 7, 2024
,
13:00
November 7, 2024
,
14:00
@
Online

Dhananjay Tomar, PhD Candidate, University of Oslo

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Visual Data Diagnosis and Debiasing with Concept Graphs

October 17, 2024

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

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Reinventing Self-Supervised Learning: The Magic of Memory in AI Training

October 17, 2024

MaSSL is a novel approach to self-supervised learning that enhances training stability and efficiency.

Recent publications

Visual Data Diagnosis and Debiasing with Concept Graphs

By authors:

Chakraborty, Rwiddhi; Wang, Yinong; Gao, Jialu; Zheng, Runkai; Zhang, Cheng; De la Torre, Fernando

Published in:

Advances in Neural Information Processing Systems

on

September 26, 2024

LSNetv2: Improving weakly supervised power line detection with bipartite matching

By authors:

Tran, Duy Khoi; Nguyen, van Nhan; Roverso, Davide; Jenssen, Robert; Kampffmeyer, Michael Christian.

Published in:

Expert Systems with Applications, Volume 250 , 2024, 123773

on

September 15, 2024

BrainIB: Interpretable brain network-based psychiatric diagnosis with graph information bottleneck

By authors:

Zheng, Kaizhong; Yu, Shujian; Li, Baojuan; Jenssen, Robert; Chen, Badong.

Published in:

IEEE Transactions on Neural Networks and Learning Systems

on

September 13, 2024

An exploratory study of self-supervised pre-training on partially supervised multi-label classification on chest X-ray images

By authors:

Zheng, Kaizhong; Yu, Shujian; Li Baojuan; Jenssen, Robert; Chen, Badong

Published in:

Applied Soft Computing, Volume 163 , 111855

on

September 1, 2024

A Spitting Image: Modular Superpixel Tokenization in Vision Transformers

By authors:

Marius Aasan, Odd Kolbjørnsen, Anne Schistad Solberg, Adín Ramirez Rivera

Published in:

ECCV (MELEX) 2024 Workshop Proceedings

on

August 28, 2024

Cross-modality sub-image retrieval using contrastive multimodal image representations

By authors:

Breznik, Eva; Wetzer, Elisabeth; Lindblad, Joakim; Sladoje, Nataša

Published in:

Scientific Reports

on

August 13, 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.

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