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Professor / Maskinlæring Institutt for fysikk og teknologi michael.c.kampffmeyer@uit.no +4777625264 Her finner du meg

Michael Kampffmeyer

Stillingsbeskrivelse

Member of the UiT Machine Learning Group. 

Personal website

 

  • Hyeongji Kim, Changkyu Choi, Michael Christian Kampffmeyer, Terje Berge, Pekka Parviainen, Ketil Malde :
    ProxyDR: Deep Hyperspherical Metric Learning with Distance Ratio-Based Formulation
    Lecture Notes in Computer Science (LNCS) 2024
  • Duy Khoi Tran, van Nhan Nguyen, Davide Roverso, Robert Jenssen, Michael Christian Kampffmeyer :
    LSNetv2: Improving weakly supervised power line detection with bipartite matching
    Expert Systems With Applications 2024 ARKIV / DOI
  • Samuel Kuttner, Luigi Tommaso Luppino, Laurence Convert, Otman Sarrhini, Roger Lecomte, Michael Christian Kampffmeyer m.fl.:
    Deep learning derived input function in dynamic [18F]FDG PET imaging of mice
    Frontiers in Nuclear Medicine 2024 ARKIV / DOI
  • Muhammad Sarmad, Michael Christian Kampffmeyer, Arnt-Børre Salberg :
    Diffusion Models with Cross-Modal Data for Super-Resolution of Sentinel-2 To 2.5 Meter Resolution
    IEEE International Geoscience and Remote Sensing Symposium proceedings 2024
  • Rwiddhi Chakraborty, Adrian Sletten, Michael Christian Kampffmeyer :
    ExMap: Leveraging Explainability Heatmaps for Unsupervised Group Robustness to Spurious Correlations
    Computer Vision and Pattern Recognition 2024
  • Srishti Gautam, Ahcene Boubekki, Marina Marie-Claire Höhne, Michael Christian Kampffmeyer :
    Prototypical Self-Explainable Models Without Re-training
    Transactions on Machine Learning Research (TMLR) 2024
  • Luoyang Lin, Zutao Jiang, Xiaodan Liang, Liqian Ma, Michael Christian Kampffmeyer, Xiaochun Cao :
    PTUS: Photo-Realistic Talking Upper-Body Synthesis via 3D-Aware Motion Decomposition Warping
    Proceedings of the AAAI Conference on Artificial Intelligence 2024
  • Changkyu Choi, Shujian Yu, Michael Christian Kampffmeyer, Arnt-Børre Salberg, Nils Olav Handegard, Robert Jenssen :
    DIB-X: Formulating Explainability Principles for a Self-Explainable Model Through Information Theoretic Learning
    Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing 2024 DOI
  • Daniel Johansen Trosten, Sigurd Eivindson Løkse, Robert Jenssen, Michael Christian Kampffmeyer :
    Leveraging tensor kernels to reduce objective function mismatch in deep clustering
    Pattern Recognition 2024 ARKIV / DOI
  • Nanqing Dong, Michael Christian Kampffmeyer, Haoyang Su, Eric Xing :
    An exploratory study of self-supervised pre-training on partially supervised multi-label classification on chest X-ray images
    Applied Soft Computing 2024 ARKIV / DOI
  • Nanqing Dong, Zhipeng Wang, Jiahao Sun, Michael Christian Kampffmeyer, William Knottenbelt, Eric Xing :
    Defending Against Poisoning Attacks in Federated Learning with Blockchain
    IEEE Transactions on Artificial Intelligence (TAI) 2024 DOI
  • Kristoffer Wickstrøm, Daniel Johansen Trosten, Sigurd Eivindson Løkse, Ahcene Boubekki, Karl Øyvind Mikalsen, Michael Kampffmeyer m.fl.:
    RELAX: Representation Learning Explainability
    International Journal of Computer Vision 2023 ARKIV / DOI
  • Changkyu Choi, Michael Kampffmeyer, Nils Olav Handegard, Arnt-Børre Salberg, Robert Jenssen :
    Deep Semisupervised Semantic Segmentation in Multifrequency Echosounder Data
    IEEE Journal of Oceanic Engineering 2023 ARKIV / DOI
  • Nanqing Dong, Michael Kampffmeyer, Irina Voiculescu, Eric Xing :
    Federated Partially Supervised Learning With Limited Decentralized Medical Images
    IEEE Transactions on Medical Imaging 2023 ARKIV / DOI
  • Daniel Johansen Trosten, Sigurd Eivindson Løkse, Robert Jenssen, Michael Kampffmeyer :
    On the Effects of Self-supervision and Contrastive Alignment in Deep Multi-view Clustering
    Computer Vision and Pattern Recognition 22. august 2023 ARKIV / DATA / DOI
  • Daniel Johansen Trosten, Rwiddhi Chakraborty, Sigurd Eivindson Løkse, Kristoffer Wickstrøm, Robert Jenssen, Michael Kampffmeyer :
    Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
    Computer Vision and Pattern Recognition 2023 ARKIV / DOI
  • Durgesh Kumar Singh, Ahcene Boubekki, Robert Jenssen, Michael Kampffmeyer :
    Supercm: Revisiting Clustering for Semi-Supervised Learning
    Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing 2023 ARKIV / DOI
  • Jonas Lederer, Michael Gastegger, Kristof T. Schütt, Michael Christian Kampffmeyer, Klaus-Robert Müller, Oliver T. Unke :
    Automatic identification of chemical moieties
    Physical Chemistry, Chemical Physics - PCCP 2023 ARKIV / DOI
  • Xujie Zhang, Binbin Yang, Michael Christian Kampffmeyer, Wenqing Zhang, Shiyue Zhang, Guansong Lu m.fl.:
    DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment
    IEEE International Conference on Computer Vision (ICCV) 2023 ARKIV
  • Luca Tomasetti, Stine Hansen, Mahdieh Khanmohammadi, Kjersti Engan, Liv Jorunn Høllesli, Kathinka Dæhli Kurz m.fl.:
    Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation
    IEEE International Symposium on Biomedical Imaging 2023 ARKIV / DOI
  • Haoyuan Li, Haoye Dong, Hanchao Jia, Dong Huang, Michael Christian Kampffmeyer, Liang Lin m.fl.:
    Coordinate Transformer: Achieving Single-stage Multi-person Mesh Recovery from Videos
    IEEE International Conference on Computer Vision (ICCV) 2023 ARKIV
  • Eirik Agnalt Østmo, Kristoffer Wickstrøm, Keyur Radiya, Michael Kampffmeyer, Robert Jenssen :
    View it like a radiologist: Shifted windows for deep learning augmentation of CT images
    Machine Learning for Signal Processing 2023 ARKIV / DOI
  • Kristoffer Wickstrøm, Eirik Agnalt Østmo, Keyur Radiya, Karl Øyvind Mikalsen, Michael Kampffmeyer, Robert Jenssen :
    A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
    Computerized Medical Imaging and Graphics 2023 ARKIV / DOI
  • Stine Hansen, Srishti Gautam, Suaiba Amina Salahuddin, Michael Christian Kampffmeyer, Robert Jenssen :
    ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement
    Medical Image Analysis 2023 ARKIV / DOI
  • Kristoffer Vinther Olesen, Ahcene Boubekki, Michael Christian Kampffmeyer, Robert Jenssen, Anders Nymark Christensen, Sune Hørlück m.fl.:
    A Contextually Supported Abnormality Detector for Maritime Trajectories
    Journal of Marine Science and Engineering (JMSE) 2023 ARKIV / DOI
  • Rogelio Andrade Mancisidor, Michael Christian Kampffmeyer, Kjersti Aas, Robert Jenssen :
    Discriminative multimodal learning via conditional priors in generative models
    Neural Networks 2023 ARKIV / DOI
  • Michael Christian Kampffmeyer, Joar Hystad :
    Michael (33) vant prestisjetung pris
    05. juni 2024
  • Petter Bjørklund, Michael Christian Kampffmeyer, Arnt-Børre Salberg, Robert Jenssen :
    Full klaff for KI-konferansen i Tromsø
    uit.no 2024
  • Michael Christian Kampffmeyer, Adrian Sletten :
    ExMap: Leveraging Explainability Heatmaps for Unsupervised Group Robustness to Spurious Correlations
    2024
  • Michael Christian Kampffmeyer :
    Towards Explainable Deep Learning Models
    2024
  • Michael Christian Kampffmeyer :
    Representation learning for deep clustering and few-shot learning
    2024
  • Michael Christian Kampffmeyer :
    Towards Self-explainable Deep Learning Models
    2024
  • Theodor Johannes Line Forgaard, Alba Ordonez, Srishti Gautam, Anders Ueland Waldeland, Jarle Hamar Reksten, Michael Christian Kampffmeyer m.fl.:
    Foundation Models for Earth Observation
    2024
  • Robert Jenssen, Michael Christian Kampffmeyer :
    Visual Intelligence Research and Innovation
    2024
  • Michael Christian Kampffmeyer :
    Michael (33) vant prestisjetung pris
    06. juni 2024
  • Robert Jenssen, Michael Christian Kampffmeyer :
    Visual Intelligence Research and Innovations
    2024
  • Ingeborg Mathiesen, Theodor Anton Ross, Anna Kaarina Pöntinen, Einar Holsbø, Michael Kampffmeyer, Mona Johannessen m.fl.:
    Characterization of Putative Virulence Factors in Enterococcus faecium
    2023
  • Changkyu Choi, Michael Christian Kampffmeyer, Nils Olav Handegard, Arnt-Børre Salberg, Robert Jenssen :
    Deep Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data
    2023
  • Michael Christian Kampffmeyer :
    Learning from limited labelled data for medical image segmentation
    2023
  • Fredrik Emil Aspheim, Luigi Tommaso Luppino, Michael Christian Kampffmeyer, Robert Jenssen, Rune Sundset, Akos Samuel Kuttner :
    Interpretable deep learning model for input function estimation in small-animal 18F-FDG PET imaging
    2023
  • Michael Christian Kampffmeyer :
    Hva er kunstig intelligens (KI)? Muligheter og utfordringer
    2023
  • Fredrik Emil Aspheim, Samuel Kuttner, Luigi Tommaso Luppino, Rune Sundset, Michael Christian Kampffmeyer, Robert Jenssen :
    Deep learning derived input-function in dynamic PET-imaging
    2023
  • Michael Christian Kampffmeyer :
    Learning from limited labeled data for few-shot medical image segmentation (and beyond)
    2023
  • Michael Christian Kampffmeyer :
    Self-Explainable Deep Learning
    2023
  • Michael Christian Kampffmeyer :
    UiT Machine Learning Group
    2023
  • Michael Christian Kampffmeyer :
    Deep Clustering
    2023
  • Magnus Oterhals Størdal, Benjamin Ricaud, Michael Christian Kampffmeyer, Geir Bertelsen, Maja Gran Erke :
    Risk Prediction of Diabetic Retinopathy in the Tromsø Study
    2023
  • Magnus Oterhals Størdal, Benjamin Ricaud, Michael Christian Kampffmeyer, Geir Bertelsen, Maja Gran Erke :
    Risk Prediction of Diabetic Retinopathy in the Tromsø Study
    2023
  • Michael Christian Kampffmeyer :
    AI’S FUTURE PATH, WHAT ARE THE OPPORTUNITIES?
    2023
  • Michael Christian Kampffmeyer :
    Deep Multi-view Clustering
    2023

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    Forskningsinteresser

    Computer Vision, Explainable AI, Medical Image Analysis, Multi-modal learning


    Medlem i forskningsgruppe

    Machine Learning   Norsk innhold