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

LSNetv2: Improving weakly supervised power line detection with bipartite matching

September 15, 2024
By
Tran, Duy Khoi; Nguyen, van Nhan; Roverso, Davide; Jenssen, Robert; Kampffmeyer, Michael Christian.

Modular Superpixel Tokenization in Vision Transformers

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

All publications

Deep-learning-derived input function in dynamic [18F]FDG PET imaging of mice

By authors:

Kuttner, Samuel; Luppino, Luigi Tommaso; Convert, Laurence; Sarrhini, Otman; Lecomte, Roger; Kampffmeyer, Michael Christian; Sundset, Rune; Jenssen, Robert.

Published in:

Frontiers in Nuclear Medicine

on

April 11, 2024

PTUS: Photo-Realistic Talking Upper-Body Synthesis via 3D-Aware Motion Decomposition Warping

By authors:

Lin, Luoyang; Jiang, Zutao; Liang, Xiaodan; Ma, Liqian; Kampffmeyer, Michael Christian; Cao, Xiaochun.

Published in:

Proceedings of the AAAI Conference on Artificial Intelligence

on

March 24, 2024

ExMap: Leveraging Explainability Heatmaps for Unsupervised Group Robustness to Spurious Correlations

By authors:

Chakraborty, Rwiddhi; Sletten, Adrian; Kampffmeyer, Michael Christian.

Published in:

Computer Vision and Pattern Recognition 2024

on

March 20, 2024

Defending Against Poisoning Attacks in Federated Learning with Blockchain

By authors:

Dong, Nanqing; Wang, Zhipeng; Sun, Jiahao; Kampffmeyer, Michael Christian; Knottenbelt, William; Xing, Eric.

Published in:

IEEE Transactions on Artificial Intelligence (TAI)

on

March 18, 2024

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

Mixed Nash for Robust Federated Learning

By authors:

Xie, Wanyun; Pethick, Thomas; Ramezani-Kebrya, Ali; Cevher, Volkan

Published in:

Transactions on Machine Learning Research (02/2024)

on

February 4, 2024

On the Generalization of Stochastic Gradient Descent with Momentum

By authors:

Ali Ramezani-Kebrya, Kimon Antonakopoulos, Volkan Cevher, Ashish Khisti, Ben Liang

Published in:

Journal of Machine Learning Research 25 (2024) 1-56

on

January 1, 2024

Prototypical Self-Explainable Models Without Re-training

By authors:

Gautam, Srishti; Boubekki, Ahcene; Höhne, Marina Marie-Claire; Kampffmeyer, Michael Christian.

Published in:

Transactions on Machine Learning Research (TMLR)

on

December 13, 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

Other publications

annual reports