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

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

SPoT: Subpixel Placement of Tokens in Vision Transformers

By authors:

Martine Hjelkrem-Tan, Marius Aasan, Gabriel Y. Arteaga, and Adín Ramírez Rivera

Published in:

Workshop on Efficient Computing under Limited Resources: Visual Computing (ICCV 2025), Oct 19 – 23th, 2025, Honolulu, Hawai'i

on

October 19, 2025

Low-Rank Adaptations for increased Generalization in Foundation Model features

By authors:

Vilde Schulerud Bøe, Andreas Kleppe, Sebastian Foersch, Daniel-Christoph Wagner, Lill-Tove Rasmussen Busund, Adín Ramírez Rivera

Published in:

MICCAI Workshop on Computational Pathology with Multimodal Data (COMPAYL), DAEJEON, South Korea, 2025

on

September 27, 2025

WiseLVAM: A Novel Framework For Left Ventricle Automatic Measurements

By authors:

Durgesh Kumar Singh, Qing Cao, Sarina Thomas, Ahcène Boubekki, Robert Jenssen, Michael Kampffmeyer

Published in:

Simplifying Medical Ultrasound, ASMUS 2025 Workshop, MICCAI 2025

on

September 17, 2025

VMRA-MaR: An Asymmetry-Aware Temporal Framework for Longitudinal Breast Cancer Risk Prediction

By authors:

Zijun Sun, Solveig Thrun and Michael Kampffmeyer

Published in:

MICCAI 2025

on

September 17, 2025

Tied Prototype Model for Few-Shot Medical Image Segmentation

By authors:

Hyeongji Kim, Stine Hansen, Michael Kampffmeyer

Published in:

MICCAI 2025

on

September 17, 2025

Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction

By authors:

Solveig Thrun, Stine Hansen, Zijun Sun, Nele Blum, Suaiba A. Salahuddin, Kristoffer Wickstrøm, Elisabeth Wetzer, Robert Jenssen, Maik Stille, Michael Kampffmeyer

Published in:

INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION (MICCAI) 2025

on

September 17, 2025

Aggregation of Dependent Expert Distributions in Multimodal Variational Autoencoders

By authors:

Rogelio A Mancisidor, Robert Jenssen, Shujian Yu, Michael Kampffmeyer

Published in:

International Conference on Machine Learning (ICLM) 2025

on

August 13, 2025

Quantifying uncertainty in foraminifera classification: How deep learning methods compare to human experts

By authors:

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

Published in:

Artificial Intelligence in Geosciences

on

July 16, 2025

Self-Organizing Visual Prototypes for Non-Parametric Representation Learning

By authors:

Thalles Silva, Helio Pedrini and Adín Ramírez Rivera

Published in:

Forty-Second International Conference on Machine Learning (ICML), Vancouver, Canada 13-19 July, 2025

on

July 13, 2025

Leveraging Foundation Model Adapters to Enable Robust and Semantic Underwater Exploration

By authors:

Changkyu Choi, Arangan Subramaniam, Nils Olav Handegard, Ali Ramezani-Kebrya and Robert Jenssen

Published in:

Proceedings of the Symposium of the Norwegian AI Society 2025, CEUR Workshop Proceedings ( ISSN 1613-0073)

on

June 17, 2025

Other publications

annual reports