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

Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images

By authors:

Luigi Tommaso Luppino, Mads Adrian Hansen, Michael Kampffmeyer, Filippo Maria Bianchi, Gabriele Moser, Robert Jenssen and Stian Normann Anfinsen

Published in:

IEEE Transactions on Neural Networks and Learning Systems 2022

on

May 12, 2022

Mitral Annulus Segmentation and Anatomical Orientation Detection in TEE Images Using Periodic 3D CNN

By authors:

Børge Solli Andreassen, David Völgyes, Eigil Samset, Anne H. Schistad Solberg

Published in:

IEEE Access, Engineering in Medicine and Biology Section

on

May 10, 2022

Demonstrating the Risk of Imbalanced Datasets in Chest X-Ray Image-Based Diagnostics by Prototypical Relevance Propagation

By authors:

Srishti Gautam, Marina M. -C. Höhne, Stine Hansen, Robert Jenssen and Michael Kampffmeyer

Published in:

IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India, 2022

on

April 26, 2022

A mammography classification model trained from image labels only

By authors:

Fredrik Dahl, Marit Holden, Olav Brautaset and Line Eikvil

Published in:

Vol. 3 (2022): Proceedings of the Northern Lights Deep Learning Workshop 2022

on

March 28, 2022

Toward Scalable and Unified Example-Based Explanation and Outlier Detection

By authors:

Penny Chong, Ngai-Man Cheung, Yuval Elovici, Alexander Binder

Published in:

IEEE Transactions on Image Processing, vol. 31, pp. 525-540, 2022

on

March 11, 2022

M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining

By authors:

Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Michael C. Kampffmeyer, Xiaoyong Wei, Minlong Lu, Yaowei Wang, Xiaodan Liang

Published in:

Conference on Computer Vision and Pattern Recognition (CVPR), 2022

on

March 3, 2022

Data-Driven Robust Control Using Reinforcement Learning

By authors:

Phuong D. Ngo, Miguel Tejedor and Fred Godtliebsen

Published in:

Appl. Sci. 2022, 12(4), 2262

on

February 21, 2022

A Pragmatic Machine Learning Approach to Quantify Tumor Infiltrating Lymphocytes in Whole Slide Images

By authors:

Nikita Shvetsov, Morten Grønnesby, Edvard Pedersen, Kajsa Møllersen, Lill-Tove Rasmussen Busund, Ruth Schwienbacher, Lars Ailo Bongo, Thomas K. Kilvaer

Published in:

Cancers 2022, 14, 2974

on

February 14, 2022

Mixing up contrastive learning: Self-supervised representation learning for time series

By authors:

Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen

Published in:

Pattern Recognition Letters, Volume 155, March 2022, Pages 54-61

on

February 12, 2022

Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels

By authors:

Stine Hansen, Srishti Gautam, Robert Jenssen, Michael Kampffmeyer

Published in:

Medical Image Analysis

on

February 11, 2022

Other publications

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