Michael Kampffmeyer

3.7k total citations · 1 hit paper
74 papers, 2.0k citations indexed

About

Michael Kampffmeyer is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Media Technology. According to data from OpenAlex, Michael Kampffmeyer has authored 74 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Artificial Intelligence, 34 papers in Computer Vision and Pattern Recognition and 11 papers in Media Technology. Recurrent topics in Michael Kampffmeyer's work include Domain Adaptation and Few-Shot Learning (14 papers), Advanced Neural Network Applications (9 papers) and Multimodal Machine Learning Applications (8 papers). Michael Kampffmeyer is often cited by papers focused on Domain Adaptation and Few-Shot Learning (14 papers), Advanced Neural Network Applications (9 papers) and Multimodal Machine Learning Applications (8 papers). Michael Kampffmeyer collaborates with scholars based in Norway, China and United States. Michael Kampffmeyer's co-authors include Robert Jenssen, Arnt-Børre Salberg, Filippo Maria Bianchi, Kristoffer Wickstrøm, Qinghui Liu, Enrico Maiorino, Antonello Rizzi, Gabriele Moser, Stian Normann Anfinsen and Luigi Tommaso Luppino and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Michael Kampffmeyer

68 papers receiving 1.9k citations

Hit Papers

Semantic Segmentation of Small Objects and Modeling of Un... 2016 2026 2019 2022 2016 100 200 300 400
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks
    2016 442 citations Michael Kampffmeyer, Arnt-Børre Salberg et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Michael Kampffmeyer Norway 21 789 641 592 216 191 74 2.0k
Xu Liu China 31 1.3k 1.6× 659 1.0× 875 1.5× 259 1.2× 226 1.2× 224 3.0k
Haikel Alhichri Saudi Arabia 24 869 1.1× 624 1.0× 815 1.4× 320 1.5× 109 0.6× 71 2.6k
Selim Aksoy Türkiye 21 1.0k 1.3× 763 1.2× 627 1.1× 241 1.1× 245 1.3× 75 2.1k
Xue Yang China 20 473 0.6× 312 0.5× 523 0.9× 304 1.4× 213 1.1× 115 2.0k
Jakub Nalepa Poland 24 849 1.1× 664 1.0× 565 1.0× 204 0.9× 200 1.0× 120 2.2k
Yangyang Li China 30 1.1k 1.4× 1.1k 1.8× 774 1.3× 242 1.1× 76 0.4× 204 3.1k
Robert Jenssen Norway 31 1.3k 1.6× 1.2k 1.9× 841 1.4× 281 1.3× 260 1.4× 123 3.6k
Xutao Li China 28 851 1.1× 1.1k 1.7× 793 1.3× 720 3.3× 110 0.6× 119 3.1k
Kai Hu China 28 885 1.1× 649 1.0× 437 0.7× 207 1.0× 41 0.2× 219 2.6k

Countries citing papers authored by Michael Kampffmeyer

Since Specialization
Citations

This map shows the geographic impact of Michael Kampffmeyer's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael Kampffmeyer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael Kampffmeyer more than expected).

Fields of papers citing papers by Michael Kampffmeyer

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael Kampffmeyer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael Kampffmeyer. The network helps show where Michael Kampffmeyer may publish in the future.

Co-authorship network of co-authors of Michael Kampffmeyer

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Kampffmeyer. A scholar is included among the top collaborators of Michael Kampffmeyer based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Michael Kampffmeyer. Michael Kampffmeyer is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Kampffmeyer, Michael, et al.. (2025). DiffFuSR: Super-Resolution of All Sentinel-2 Multispectral Bands Using Diffusion Models. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–13. 1 indexed citations
2.
Lang, Congyan, et al.. (2025). UNAGI: Unified neighbor-aware graph neural network for multi-view clustering. Neural Networks. 185. 107193–107193.
3.
Zhang, Yujia, et al.. (2024). Prompt-guided bidirectional deep fusion network for referring image segmentation. Neurocomputing. 616. 128899–128899. 2 indexed citations
4.
Luppino, Luigi Tommaso, et al.. (2024). Deep-learning-derived input function in dynamic [18F]FDG PET imaging of mice. SHILAP Revista de lepidopterología. 4. 1372379–1372379. 1 indexed citations
5.
Wickstrøm, Kristoffer, et al.. (2023). RELAX: Representation Learning Explainability. International Journal of Computer Vision. 131(6). 1584–1610. 4 indexed citations
6.
Lederer, Jonas, Michael Gastegger, Kristof T. Schütt, et al.. (2023). Automatic identification of chemical moieties. Physical Chemistry Chemical Physics. 25(38). 26370–26379. 7 indexed citations
7.
Wickstrøm, Kristoffer, et al.. (2023). Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-Shot Learning with Hyperspherical Embeddings. Duo Research Archive (University of Oslo). 7527–7536. 13 indexed citations
8.
Wickstrøm, Kristoffer, et al.. (2023). A clinically motivated self-supervised approach for content-based image retrieval of CT liver images. Computerized Medical Imaging and Graphics. 107. 102239–102239. 10 indexed citations
9.
Kampffmeyer, Michael, et al.. (2023). Discriminative multimodal learning via conditional priors in generative models. Neural Networks. 169. 417–430. 4 indexed citations
10.
Hansen, Stine Thestrup, et al.. (2023). Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation. Duo Research Archive (University of Oslo). 1–5. 5 indexed citations
11.
Hansen, Stine, et al.. (2023). ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement. Medical Image Analysis. 89. 102870–102870. 9 indexed citations
12.
Luppino, Luigi Tommaso, Michael Kampffmeyer, Filippo Maria Bianchi, et al.. (2022). Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images. IEEE Transactions on Neural Networks and Learning Systems. 35(1). 60–72. 100 indexed citations
13.
Hansen, Stine, et al.. (2022). Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels. Medical Image Analysis. 78. 102385–102385. 66 indexed citations
14.
Dong, Nanqing, Michael Kampffmeyer, Irina Voiculescu, & Eric P. Xing. (2022). Negational symmetry of quantum neural networks for binary pattern classification. Pattern Recognition. 129. 108750–108750. 5 indexed citations
15.
Luppino, Luigi Tommaso, Michael Kampffmeyer, Filippo Maria Bianchi, et al.. (2021). Deep Image Translation With an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection. IEEE Transactions on Geoscience and Remote Sensing. 60. 1–22. 130 indexed citations
16.
Kampffmeyer, Michael, et al.. (2020). Learning latent representations of bank customers with the Variational Autoencoder. Duo Research Archive (University of Oslo). 22 indexed citations
17.
Kampffmeyer, Michael, et al.. (2020). Deep generative models for reject inference in credit scoring. Duo Research Archive (University of Oslo). 37 indexed citations
18.
Eikvil, Line, et al.. (2020). Explaining decisions of deep neural networks used for fish age prediction. PLoS ONE. 15(6). e0235013–e0235013. 20 indexed citations
19.
Liu, Qinghui, Michael Kampffmeyer, Robert Jenssen, & Arnt-Børre Salberg. (2019). Road Mapping in Lidar Images Using a Joint-Task Dense Dilated Convolutions Merging Network. 9. 5041–5044. 3 indexed citations
20.
Mikalsen, Karl Øyvind, et al.. (2018). Towards deep anchor learning. 315–318.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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