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 blog posts

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

Modular Superpixel Tokenization in Vision Transformers

August 28, 2024
By
Marius Aasan, Odd Kolbjørnsen, Anne Schistad Solberg, Adín Ramirez Rivera

All publications

Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images

By authors:

Qinghui Liu (Brian), Michael Kampffmeyer, Robert Jenssen, Arnt-Børre Salberg

Published in:

International Journal of Remote Sensing

on

June 16, 2021

RadioUNet: Fast Radio Map Estimation With Convolutional Neural Networks

By authors:

Ron Levie , Çagkan Yapar , Gitta Kutyniok, and Giuseppe Caire

Published in:

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 20, NO. 6, JUNE 2021

on

June 6, 2021

Reconsidering Representation Alignment for Multi-view Clustering

By authors:

Daniel J. Trosten, Sigurd Løkse, Robert Jenssen, Michael Kampffmeyer

Published in:

CVPR 2021

on

March 13, 2021

Morphological and molecular breast cancer profiling through explainable machine learning

By authors:

Alexander Binder, Michael Bockmayr, Miriam Hägele, Stephan Wienert, Daniel Heim, Katharina Hellweg, Masaru Ishii, Albrecht Stenzinger, Andreas Hocke, Carsten Denkert, Klaus-Robert Müller & Frederick Klauschen

Published in:

Nature Machine Intelligence volume 3, pages 355–366 (2021)

on

March 8, 2021

Pruning by explaining: A novel criterion for deep neural network prunin

By authors:

Yeom, Seul-Ki; Seegerer, Philipp; Lapuschkin, Sebastian; Binder, Alexander; Wiedemann, Simon; Müller, Klaus-Robert; Samek, Wojciech.

Published in:

Pattern Recognition

on

March 3, 2021

Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection

By authors:

Luppino, Luigi Tommaso; Kampffmeyer, Michael; Bianchi, Filippo Maria; Moser, Gabriele; Serpico, Sebastiano Bruno; Jenssen, Robert; Anfinsen, Stian Normann

Published in:

IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-22, 2022

on

February 17, 2021

Cerebral blood flow measurements with 15O-water PET using a non-invasive machine-learning-derived arterial input function

By authors:

Kuttner, Samuel; Wickstrøm, Kristoffer Knutsen; Lubberink, Mark; Tolf, Andreas; Burman, Joachim; Sundset, Rune; Jenssen, Robert; Appel, Lieuwe; Axelsson, Jan

Published in:

Journal of Cerebral Blood Flow and Metabolism 2021 s. 1-13

on

February 8, 2021

Measuring Dependence with Matrix‐Based Entropy Functional

By authors:

Shujian Yu, Francesco Alesiani, Xi Yu, Robert Jenssen, Jose Principe

Published in:

AAAI 2021

on

January 25, 2021

Curve-Based Classification Approach for Hyperspectral Dermatologic Data Processing

By authors:

Uteng, Stig; Quevedo, Eduardo; Callico, Gustavo M.; Castaño, Irene; Carretero, Gregorio; Almeida, Pablo; Garcia, Aday; Hernandez, Javier A.; Godtliebsen, Fred.

Published in:

Sensors

on

January 20, 2021

Uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps

By authors:

Kristoffer Wickstrøm, Michael Kampffmeyer, Robert Jenssen

Published in:

Medical Image Analysis, Volume 60, February 2020, 101619

on

November 14, 2019

Other publications

annual reports