Scientific publications

At Visual Intelligence we work across our innovation areas to extract knowledge from large volumes of visual data more efficiently through automatic and intelligent data analysis. The work to address the core research challenges in deep learning: working with limited training data, utilizing context and dependencies, providing explainability, confidence and uncertainty, are important in all the innovation areas.

Featured papers

AI matches human experts in classifying microscopic organisms

July 16, 2025
By
Iver Martinsen, Steffen Aagaard Sørensen, Samuel Ortega, Fred Godtliebsen, Miguel Tejedor, Eirik Myrvoll-Nilsen

Visual Data Diagnosis and Debiasing with Concept Graphs

September 26, 2024
By
Chakraborty, Rwiddhi; Wang, Yinong; Gao, Jialu; Zheng, Runkai; Zhang, Cheng; De la Torre, Fernando

All publications

Interrogating Sea Ice Predictability With Gradients

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

Cauchy-Schwarz Divergence Information Bottleneck for Regression

By authors:

Yu, Shujian; Løkse, Sigurd Eivindson; Jenssen, Robert; Principe, Jose.

Published in:

International Conference on Learning Representations 2024

on

January 16, 2024

A self-supervised inspired object scoring system for building change detection

By authors:

Jensen, Are Charles

Published in:

Proceedings of Machine Learning Research (PMLR) ISSN 2640-3498. 233, p. 97–103

on

January 8, 2024

A two-stage mammography classification model using explainable-AI for ROI detection

By authors:

Fredrik Andreas Dahl, Olav Brautaset, Marit Holden, Line Eikvil, Marthe Larsen, Solveig Sand-Hanssen Hofvind

Published in:

Vol. 3 No. 2 (2023): Proceedings of NORA annual conference 2023

on

November 17, 2023

Discriminative multimodal learning via conditional priors in generative models

By authors:

Rogelio A. Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen,

Published in:

Neural Networks, Volume 169, 2024, Pages 417-430

on

November 4, 2023

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

OVERVIEW OF THE URBAN WIRELESS LOCALIZATION COMPETITION

By authors:

C¸ agkan Yapar, Fabian Jaensch, Ron Levie, Gitta Kutynio, Giuseppe Caire

Published in:

2023 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, SEPT. 17–20, 2023, ROME, ITALY

on

September 1, 2023

Other publications

annual reports