Fabian Hüger

599 total citations
20 papers, 197 citations indexed

About

Fabian Hüger is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Automotive Engineering. According to data from OpenAlex, Fabian Hüger has authored 20 papers receiving a total of 197 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Computer Vision and Pattern Recognition, 11 papers in Artificial Intelligence and 3 papers in Automotive Engineering. Recurrent topics in Fabian Hüger's work include Advanced Neural Network Applications (9 papers), Adversarial Robustness in Machine Learning (6 papers) and Anomaly Detection Techniques and Applications (4 papers). Fabian Hüger is often cited by papers focused on Advanced Neural Network Applications (9 papers), Adversarial Robustness in Machine Learning (6 papers) and Anomaly Detection Techniques and Applications (4 papers). Fabian Hüger collaborates with scholars based in Germany, United States and Switzerland. Fabian Hüger's co-authors include Peter Schlicht, Tim Fingscheidt, Nico M. Schmidt, Hanno Gottschalk, Matthias Rottmann, Robin Chan, Ján Schneider, Michael Weber, J. Marius Zöllner and Thomas Stauner and has published in prestigious journals such as IEEE Signal Processing Magazine, ATZ - Automobiltechnische Zeitschrift and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

In The Last Decade

Fabian Hüger

19 papers receiving 191 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fabian Hüger Germany 9 140 83 24 12 11 20 197
Éloi Zablocki France 7 94 0.7× 107 1.3× 59 2.5× 8 0.7× 8 0.7× 11 214
Sugiri Pranata Singapore 6 287 2.0× 61 0.7× 13 0.5× 16 1.3× 5 0.5× 12 335
Jing Yu Koh United States 8 257 1.8× 83 1.0× 17 0.7× 9 0.8× 6 0.5× 9 320
Soumava Kumar Roy Australia 9 189 1.4× 67 0.8× 7 0.3× 19 1.6× 8 0.7× 16 264
Jacob Walker United States 4 160 1.1× 79 1.0× 24 1.0× 6 0.5× 3 0.3× 5 203
Vineet Gandhi India 10 229 1.6× 54 0.7× 20 0.8× 15 1.3× 5 0.5× 37 284
Akio Yoneyama Japan 9 139 1.0× 54 0.7× 18 0.8× 11 0.9× 12 1.1× 28 244
Junhyuk Hyun South Korea 10 165 1.2× 70 0.8× 12 0.5× 20 1.7× 6 0.5× 18 211
Yinglin Zheng China 7 311 2.2× 125 1.5× 12 0.5× 11 0.9× 5 0.5× 14 373

Countries citing papers authored by Fabian Hüger

Since Specialization
Citations

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

Fields of papers citing papers by Fabian Hüger

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fabian Hüger

This figure shows the co-authorship network connecting the top 25 collaborators of Fabian Hüger. A scholar is included among the top collaborators of Fabian Hüger 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 Fabian Hüger. Fabian Hüger 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.
Chan, Robin, Matthias Rottmann, Peter Schlicht, et al.. (2023). What should AI see? Using the public’s opinion to determine the perception of an AI. AI and Ethics. 3(4). 1381–1405. 3 indexed citations
2.
Hüger, Fabian, et al.. (2022). Performance Prediction for Semantic Segmentation by a Self-Supervised Image Reconstruction Decoder. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 4398–4407. 1 indexed citations
3.
Hüger, Fabian, et al.. (2022). Methodik zur Absicherung von KI im Fahrzeug. ATZ - Automobiltechnische Zeitschrift. 124(7-8). 58–63.
4.
Hüger, Fabian, et al.. (2022). Traffic Sign Classifiers Under Physical World Realistic Sticker Occlusions: A Cross Analysis Study. 2022 IEEE Intelligent Vehicles Symposium (IV). 644–650. 3 indexed citations
5.
Hüger, Fabian, et al.. (2022). Assurance Methodology for In-vehicle AI. ATZ worldwide. 124(7-8). 54–59. 3 indexed citations
6.
7.
Rottmann, Matthias, et al.. (2021). Improving Video Instance Segmentation by Light-weight Temporal Uncertainty Estimates. 1–8. 5 indexed citations
8.
Schmidt, Nico M., et al.. (2020). Focussing Learned Image Compression to Semantic Classes for V2X Applications. 1641–1648. 13 indexed citations
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10.
Weber, Michael, et al.. (2020). Using Mixture of Expert Models to Gain Insights into Semantic Segmentation. 1399–1406. 16 indexed citations
11.
Chan, Robin, Matthias Rottmann, Hanno Gottschalk, Fabian Hüger, & Peter Schlicht. (2020). Application of Maximum Likelihood Decision Rules for Handling Class Imbalance in Semantic Segmentation. Proceedings of the 30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference. 3065–3072. 8 indexed citations
12.
Chan, Robin, Matthias Rottmann, Fabian Hüger, Peter Schlicht, & Hanno Gottschalk. (2020). Controlled False Negative Reduction of Minority Classes in Semantic Segmentation. 1–8. 6 indexed citations
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14.
Schneider, Ján, et al.. (2020). Unsupervised Temporal Consistency Metric for Video Segmentation in Highly-Automated Driving. 1369–1378. 22 indexed citations
15.
Hüger, Fabian, et al.. (2020). The Vulnerability of Semantic Segmentation Networks to Adversarial Attacks in Autonomous Driving: Enhancing Extensive Environment Sensing. IEEE Signal Processing Magazine. 38(1). 42–52. 27 indexed citations
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Hüger, Fabian, et al.. (2019). On the Robustness of Redundant Teacher-Student Frameworks for Semantic Segmentation. 1380–1388. 13 indexed citations
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Hüger, Fabian. (2011). User interface transfer for driver information systems. 113–120. 4 indexed citations

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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