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.
May 12, 2022
PhD student Srishti Gautam in Visual Intelligence and her collaborators from UiT The Arctic University of Norway and Technical University of Berlin got their paper "ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model" accepted to NeurIPS 2022!
Developing deep learning-based AI for quantitative PET imaging. Groups joining forces and a new innovation project.
This conference will gather researchers both on a national and international level to exchange ideas, encourage collaborations and present cutting-edge research.
ProtoVAE explainability paper by Srishti Gautam and co-authors is published to NeurIPS 2022.
We present a novel pyramid attention and gated fusion method (MultiModNet) for multi-modality land cover mapping in remote sensing.
By authors:
Srishti Gautam, Ahcene Boubekki, Stine Hansen, Suaiba Salahuddin, Robert Jenssen, Marina Höhne, Michael Kampffmeyer
Published in:
NeurIPS 2022
on
September 15, 2022
By authors:
Xujie Zhang, Yu Sha, Michael Kampffmeyer, Zhenyu Xie, Zequn Jie, Chengwen Huang, Jianqing Peng, Xiaodan Liang
Published in:
ACM Multimedia (ACM MM 2022)
on
August 11, 2022
By authors:
Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg
Published in:
International Journal of Remote Sensing, 2022
on
July 1, 2022
By authors:
Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu
Published in:
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2022)
on
June 30, 2022
By authors:
Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen and Jose C. Principe
Published in:
Conference on Uncertainty in Artificial Intelligence (UAI) 2022
on
May 20, 2022
By authors:
Børge Solli Andreassen, David Völgyes, Eigil Samset, Anne H. Schistad Solberg
Published in:
IEEE Access, Engineering in Medicine and Biology Section
on
May 10, 2022
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.
