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.
April 11, 2024
The company's interest in VI is to utilize new developments in artificial intelligence (AI) to improve and automate interpretations of data, while providing the centre with data, problem formulations, and different forms of guidance.
The presented research is part of a larger collaboration between researchers from UiT The Arctic University of Norway and the University Hospital of North Norway.
Rwiddhi Chakraborty, Doctoral Research Fellow at theMachine Learning Group, UiT The Arctic University of Norway
The paper focuses on interrogating the effect of the IceNet's input feature with a gradient-based analysis.
We propose DeepMVC – a unified framework which includes many recent methods as instances.
By authors:
Joakimsen, H. L., Martinsen I., Luppino, L. T., McDonald, A., Hosking, S., and Jenssen, R.
Published in:
IEEE Geoscience and Remote Sensing Letters
on
February 14, 2024
By authors:
Xie, Wanyun; Pethick, Thomas; Ramezani-Kebrya, Ali; Cevher, Volkan
Published in:
Transactions on Machine Learning Research (02/2024)
on
February 4, 2024
By authors:
Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang
Published in:
Journal of Machine Learning Research 25 (2024) 1-56
on
January 1, 2024
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
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
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
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 moreWe 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.