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

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

Pixel-Level Predictions with Embedded Lookup Tables

By authors:

Marius Aasan, Adín Ramírez Rivera

Published in:

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

on

June 17, 2025

Assessing the Efficacy of Multi-task Learning in Mammographic Density Classification: A Study on Class Imbalance and Model Performance

By authors:

Suaiba A. Salahuddin, Elisabeth Wetzer, Kristoffer Wickstrøm, Solveig Thrun, Michael Kampffmeyer and Robert Jenssen

Published in:

Lecture Notes in Computer Science (LNCS) 2025 ;Volum 15726.

on

June 16, 2025

Interactive Injectite Mapping with Minimal Training Data using Self-Supervised Learning

By authors:

A. Waldeland, T.J.L. Forgaard, A. Ordonez, D. Wade and A.J. Bugge

Published in:

86th EAGE Annual Conference & Exhibition, Jun 2025, Volume 2025, p.1 - 5

on

June 2, 2025

ProxyDR: Deep Hyperspherical Metric Learning with Distance Ratio-Based Formulation

By authors:

Hyeongji Kim, Changkyu Choi, Michael Christian Kampffmeyer, Terje Berge, Pekka Parviainen, Ketil Malde

Published in:

Lecture Notes in Computer Science (LNCS) 2025

on

May 12, 2025

Addressing Label Shift in Distributed Learning via Entropy Regularization​

By authors:

Zhiyuan Wu, Changkyu Choi, Volkan Cevher, Ali Ramezani-Kebrya

Published in:

International Conference on Learning Representations 2025

on

April 29, 2025

Other publications

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