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Department of Physics and Technology michael.c.kampffmeyer@uit.no +4777625264 Tromsø FPARK B 276

Michael Kampffmeyer


Associate Professor / Group Leader Machine Learning

Job description

Member of the UiT Machine Learning Group.

Personal website


  • Xujie Zhang, Yu Sha, Michael Kampffmeyer, Zhenyu Xie, Zequn Jie, Chengwen Huang et al.:
    ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design
    SIGMM Records 2022
  • Kristoffer Wickstrøm, Michael Kampffmeyer, Karl Øyvind Mikalsen, Robert Jenssen :
    Mixing up contrastive learning: Self-supervised representation learning for time series
    Pattern Recognition Letters 2022 ARKIV / DOI
  • Luigi Tommaso Luppino, Mads Adrian Hansen, Michael Kampffmeyer, Filippo Maria Bianchi, Gabriele Moser, Robert Jenssen et al.:
    Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
    IEEE Transactions on Neural Networks and Learning Systems 2022 DOI
  • Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric Xing :
    Negational symmetry of quantum neural networks for binary pattern classification
    Pattern Recognition 2022 DOI
  • Nanqing Dong, Michael Kampffmeyer, Xiaodan Liang, Min Xu, Irina Voiculescu, Eric Xing :
    Towards robust partially supervised multi-structure medical image segmentation on small-scale data
    Applied Soft Computing 2022 ARKIV / DOI
  • Rogelio Andrade Mancisidor, Michael Kampffmeyer, Kjersti Aas, Robert Jenssen :
    Generating customer's credit behavior with deep generative models
    Knowledge-Based Systems 2022 ARKIV / DOI
  • Stine Hansen, Srishti Gautam, Robert Jenssen, Michael Kampffmeyer :
    Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels
    Medical Image Analysis 2022 ARKIV / DOI
  • Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt Børre Salberg :
    Multi-modal land cover mapping of remote sensing images using pyramid attention and gated fusion networks
    International Journal of Remote Sensing 2022 DOI
  • Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu :
    Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
    Lecture Notes in Computer Science (LNCS) 2022
  • Srishti Gautam, Marina Marie-Claire Hohne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer :
    Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
    IEEE International Symposium on Biomedical Imaging 2022 DOI
  • Srishti Gautam, Marina Marie-Claire Hohne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer :
    This looks more like that: Enhancing Self-Explaining Models by Prototypical Relevance Propagation
    Pattern Recognition 2022 ARKIV / DOI
  • Srishti Gautam, Ahcene Boubekki, Stine Hansen, Suaiba Amina Salahuddin, Robert Jenssen, Marina Marie-Claire Hohne et al.:
    ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model
    Advances in Neural Information Processing Systems 2022
  • Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Michael Kampffmeyer, Xiaoyong Wei et al.:
    M5Product: Self-harmonized Contrastive Learning for E-commercial Multi-modal Pretraining
    Computer Vision and Pattern Recognition 2022
  • Zaiyu Huang, Hanhui Li, Zhenyu Xie, Michael Kampffmeyer, Xiaodan Liang :
    Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning
    Advances in Neural Information Processing Systems 2022
  • Stine Hansen, Srishti Gautam, Michael Kampffmeyer, Robert Jenssen :
    A self-guided anomaly detection-inspired few-shot segmentation network
    CEUR Workshop Proceedings 2022
  • Suaiba Amina Salahuddin, Stine Hansen, Srishti Gautam, Michael Kampffmeyer, Robert Jenssen :
    A self-guided anomaly detection-inspired few-shot segmentation network
    CEUR Workshop Proceedings 2022
  • Stine Hansen, Srishti Gautam, Michael Kampffmeyer, Robert Jenssen :
    A self-guided anomaly detection-inspired few-shot segmentation network
    CEUR Workshop Proceedings 2022
  • Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Eivindson Løkse, Gusatu Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    The Kernelized Taylor Diagram
    Communications in Computer and Information Science 2022
  • Ahcene Boubekki, Michael Kampffmeyer, Ulf Brefeld, Robert Jenssen :
    Joint optimization of an autoencoder for clustering and embedding.
    Machine Learning 2021 ARKIV / DOI
  • Changkyu Choi, Michael Kampffmeyer, Nils Olav Handegard, Arnt Børre Salberg, Olav Brautaset, Line Eikvil et al.:
    Semi-supervised target classification in multi-frequency echosounder data
    ICES Journal of Marine Science 12. August 2021 ARKIV / FULLTEKST / DOI
  • Fuwei Zhao, Zhenyu Xie, Michael Kampffmeyer, Haoye Dong, Songfang Han, Tianxiang Zheng et al.:
    M3D-VTON: A Monocular-to-3D Virtual Try-On Network
    IEEE International Conference on Computer Vision (ICCV) 2021 ARKIV / DOI
  • Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, Xiaodan Liang :
    Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN
    Advances in Neural Information Processing Systems 2021 ARKIV / DOI
  • Daniel Johansen Trosten, Sigurd Eivindson Løkse, Robert Jenssen, Michael Kampffmeyer :
    Reconsidering Representation Alignment for Multi-View Clustering
    Computer Vision and Pattern Recognition 2021 ARKIV / DOI
  • Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu :
    Self-supervised Multi-task Representation Learning for Sequential Medical Images
    Lecture Notes in Computer Science (LNCS) 2021 DOI
  • Kristoffer Knutsen Wickstrøm, Karl Oyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen :
    Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series
    IEEE journal of biomedical and health informatics 2021 ARKIV / DOI
  • Luigi Tommaso Luppino, Michael Kampffmeyer, Filippo Maria Bianchi, Gabriele Moser, Sebastiano Bruno Serpico, Robert Jenssen et al.:
    Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection
    IEEE Transactions on Geoscience and Remote Sensing 2021 ARKIV / DOI
  • Daniel Johansen Trosten, Robert Jenssen, Michael Kampffmeyer :
    Reducing Objective Function Mismatch in Deep Clustering with the Unsupervised Companion Objective
    Proceedings of the Northern Lights Deep Learning Workshop 2021 ARKIV / DOI
  • Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt Børre Salberg :
    Self-constructing graph neural networks to model long-range pixel dependencies for semantic segmentation of remote sensing images
    International Journal of Remote Sensing 2021 ARKIV / DOI
  • Kristoffer Wickstrøm, Eirik Agnalt Østmo, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    Explaining representations for medical image retrieval
    2022
  • Kristoffer Knutsen Wickstrøm, Daniel Johansen Trosten, Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    RELAX: Representation Learning Explainability
    2022
  • Daniel Johansen Trosten, Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    RELAX: Representation Learning Explainability
    2022
  • Daniel Johansen Trosten, Kristoffer Wickstrøm, Shujian Yu, Sigurd Eivindson Løkse, Robert Jenssen, Michael Kampffmeyer :
    Deep Clustering with the Cauchy-Schwarz Divergence
    2022
  • Samuel Kuttner, Luigi Tommaso Luppino, Kristoffer Wickstrøm, Nils Thomas Doherty Midtbø, Seyed Esmaeil Dorraji, Ana Oteiza et al.:
    Deep learning derived input function in dynamic 18F-FDG PET imaging of mice
    2022
  • Kristoffer Wickstrøm, Juan Emmanuel Johnson, Sigurd Eivindson Løkse, Gusatu Camps-Valls, Karl Øyvind Mikalsen, Michael Kampffmeyer et al.:
    The Kernelized Taylor Diagram
    2022
  • Srishti Gautam, Marina Marie-Claire Hohne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer :
    Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
    2022
  • Srishti Gautam, Marina Marie-Claire Hohne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer :
    Demonstrating The Risk of Imbalanced Datasets in Chest X-ray Image-based Diagnostics by Prototypical Relevance Propagation
    2022
  • Srishti Gautam, Marina Marie-Claire Hohne, Stine Hansen, Robert Jenssen, Michael Kampffmeyer :
    Artifact Detection with Prototypical Relevance Propagation
    2022
  • Michael Kampffmeyer :
    Fikk 12 millioner til bildetolking
    06. January 2021
  • Nils Olav Handegard, Line Eikvil, Robert Jenssen, Michael Kampffmeyer, Arnt Børre Salberg, Ketil Malde :
    Machine Learning + Marine Science: Critical Role of Partnerships in Norway
    Journal of Ocean Technology 2021 ARKIV
  • Michael Kampffmeyer :
    UiT-gruppe får millionstøtte for å tolke bilder
    11. January 2021
  • Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    Towards Explainable Representation Learning
    2021
  • Michael Kampffmeyer :
    Deep Clustering
    2021
  • Kristoffer Knutsen Wickstrøm, Michael Kampffmeyer, Robert Jenssen :
    Advances in explainable DL & how to model uncertainty in explainability
    2021
  • Kristoffer Knutsen Wickstrøm, Karl Øyvind Mikalsen, Michael Kampffmeyer, Arthur Revhaug, Robert Jenssen :
    Uncertainty-Aware Deep Ensembles for Explainable Time Series Prediction
    2021
  • Qinghui Liu, Michael Kampffmeyer, Robert Jenssen, Arnt Børre Salberg :
    PAGNet Models for The 2nd Agriculture-Vision Challenges CVPR 2021
  • Changkyu Choi, Michael Kampffmeyer, Nils Olav Handegard, Arnt Børre Salberg, Line Eikvil, Robert Jenssen :
    Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data
    2021
  • Sigurd Eivindson Løkse, Michael Kampffmeyer, Robert Jenssen, Karl Øyvind Mikalsen :
    Towards Explainable Representation Learning
    2021
  • Stine Hansen, Srishti Gautam, Robert Jenssen, Michael Kampffmeyer :
    Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision
    2021
  • Michael Kampffmeyer, Robert Jenssen, Karl Øyvind Mikalsen, Sigurd Eivindson Løkse :
    Towards Explainable Representation Learning
    2021
  • Kristoffer Knutsen Wickstrøm, Sigurd Eivindson Løkse, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    Towards Explainable Representation Learning
    2021

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    FPARK B 276

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