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

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

A Hubness Perspective on Representation Learning for Graph-Based Multi-View Clustering

By authors:

Zheming Xu, He Liu, Congyan Lang, Tao Wang, Yidong Li, Michael Kampffmeyer

Published in:

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2025, pp. 15528-15537

on

June 11, 2025

AdaptCMVC: Robust Adaption to Incremental Views in Continual Multi-view Clustering

By authors:

Jing Wang, Songhe Feng, Kristoffer Wickstrøm, Michael Kampffmeyer

Published in:

IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10285-10294, 2025

on

June 10, 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

Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility Through Quantization

By authors:

Holger Boche, Vit Fojtik, Adalbert Fono,Gitta Astrid Hildegard Kutyniok

Published in:

Journal of Fourier Analysis and Applications 31, 35 (2025)

on

May 29, 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

Robust Classification by Coupling Data Mollification with Label Smoothing

By authors:

Markus Heinonen, Ba-Hien Tran, Michael Kampffmeyer, Maurizio Filippone

Published in:

Proceedings of Machine Learning Research (PMLR), Volume 258, pp4960-4968, 2025

on

May 3, 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

Sitcom-Crafter: A Plot-Driven Human Motion Generation System in 3D Scenes

By authors:

Jianqi Chen, Panwen Hu, Xiaojun Chang, Zhenwei Shi, Michael Kampffmeyer, Xiaodan Liang

Published in:

International Conference on Learning Representations (ICLR) 2025

on

April 24, 2025

Robust Identifiability for Symbolic Recovery of Differential Equations

By authors:

Hillary Hauger, Philipp Scholl, Gitta Astrid Hildegard Kutyniok

Published in:

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025, pp. 1-5

on

April 11, 2025

Other publications

annual reports