Marco Rudolph

597 total citations · 2 hit papers
8 papers, 312 citations indexed

About

Marco Rudolph is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Marco Rudolph has authored 8 papers receiving a total of 312 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 2 papers in Computer Networks and Communications. Recurrent topics in Marco Rudolph's work include Anomaly Detection Techniques and Applications (4 papers), Industrial Vision Systems and Defect Detection (2 papers) and Network Security and Intrusion Detection (2 papers). Marco Rudolph is often cited by papers focused on Anomaly Detection Techniques and Applications (4 papers), Industrial Vision Systems and Defect Detection (2 papers) and Network Security and Intrusion Detection (2 papers). Marco Rudolph collaborates with scholars based in Germany, Sweden and Canada. Marco Rudolph's co-authors include Bodo Rosenhahn, Bastian Wandt, Tom Wehrbein, Geoffrey Swain, Axel Rosenhahn and Kelli Z. Hunsucker and has published in prestigious journals such as IEEE Transactions on Robotics, Biofouling and 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV).

In The Last Decade

Marco Rudolph

7 papers receiving 310 citations

Hit Papers

Fully Convolutional Cross-Scale-Flows for Image-based Def... 2022 2026 2023 2024 2022 2023 50 100 150
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. Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection
    2022 192 citations Marco Rudolph, Tom Wehrbein et al. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) profile →
  2. Asymmetric Student-Teacher Networks for Industrial Anomaly Detection
    2023 103 citations Marco Rudolph, Tom Wehrbein et al. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) profile →

Peers

Marco Rudolph
Bastian Wandt Sweden
Tom Wehrbein Germany
Denis Gudovskiy United States
Shun Ishizaka Japan
Yunkang Cao China
Hanqiu Deng Canada
Sindy Löwe Netherlands
Neelu Madan Denmark
Guoyang Xie China
Bastian Wandt Sweden
Marco Rudolph
Citations per year, relative to Marco Rudolph Marco Rudolph (= 1×) peers Bastian Wandt

Countries citing papers authored by Marco Rudolph

Since Specialization
Citations

This map shows the geographic impact of Marco Rudolph'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 Marco Rudolph with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Marco Rudolph more than expected).

Fields of papers citing papers by Marco Rudolph

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Marco Rudolph. 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 Marco Rudolph. The network helps show where Marco Rudolph may publish in the future.

Co-authorship network of co-authors of Marco Rudolph

This figure shows the co-authorship network connecting the top 25 collaborators of Marco Rudolph. A scholar is included among the top collaborators of Marco Rudolph 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 Marco Rudolph. Marco Rudolph is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

8 of 8 papers shown
1.
Wehrbein, Tom, et al.. (2025). Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery. 5852–5862. 1 indexed citations
2.
Rudolph, Marco. (2024). Industrial Anomaly Detection with Normalizing Flows. VDI Verlag eBooks.
3.
Rudolph, Marco, et al.. (2024). SplatPose & Detect: Pose-Agnostic 3D Anomaly Detection. 3950–3960. 1 indexed citations
4.
Rudolph, Marco, et al.. (2024). Q-SENN: Quantized Self-Explaining Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence. 38(19). 21482–21491. 4 indexed citations
5.
Rudolph, Marco, et al.. (2023). Semantic segmentation for fully automated macrofouling analysis on coatings after field exposure. Biofouling. 39(1). 64–79. 4 indexed citations
6.
Rudolph, Marco, et al.. (2023). The voraus-AD Dataset for Anomaly Detection in Robot Applications. IEEE Transactions on Robotics. 40. 438–451. 7 indexed citations
7.
Rudolph, Marco, Tom Wehrbein, Bodo Rosenhahn, & Bastian Wandt. (2023). Asymmetric Student-Teacher Networks for Industrial Anomaly Detection. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 2591–2601. 103 indexed citations breakdown →
8.
Rudolph, Marco, Tom Wehrbein, Bodo Rosenhahn, & Bastian Wandt. (2022). Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection. 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 1829–1838. 192 indexed citations breakdown →

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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