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

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

M3D-VTON: A Monocular-to-3D Virtual Try-On Network

By authors:

Zhao, Fuwei; Xie, Zhenyu; Kampffmeyer, Michael; Dong, Haoye; Han, Songfang; Zheng, Tianxiang; Zhang, Tao; Liang, Xiaodan

Published in:

IEEE International Conference on Computer Vision (ICCV). 2021

on

February 28, 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

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 International Symposium on Biomedical Imaging (ISBI) 2022

on

February 1, 2022

Explain and improve: LRP-inference fine-tuning for image captioning models

By authors:

Sun, Jiamei; Lapuschkin, Sebasian; Samek, Wojciech; Binder, Alexander.

Published in:

Information Fusion, Volume 77, 2022, Pages 233-246

on

January 1, 2022

Other publications

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