Timo Lüddecke
Impact in
-
- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Human Pose and Action Recognition
- Generative Adversarial Networks and Image Synthesis
- Artificial Intelligence top 10%
- Domain Adaptation and Few-Shot Learning
- Natural Language Processing Techniques
Papers in
-
- Human Pose and Action Recognition 4
- Multimodal Machine Learning Applications 3
- Robotic Path Planning Algorithms 2
- Advanced Neural Network Applications 2
- Advanced Image and Video Retrieval Techniques 2
-
- Robot Manipulation and Learning 3
- Co-authors
- Alexander S. Ecker (3 shared papers)Florentin Wörgötter (9 shared papers)Tomas Kulvičius (3 shared papers)Minija Tamošiūnaitė (3 shared papers)Alejandro Agostini (1 shared paper)Michael Fauth (1 shared paper)Kai Han (1 shared paper)Samuel Albanie (1 shared paper)
In The Last Decade
Timo Lüddecke
13 papers receiving 343 citations
Timo Lüddecke's Hit Papers
Peers
Comparison fields: 5 of 73
- Computer Vision and Pattern Recognition 219
- Artificial Intelligence 111
- Computer Graphics and Computer-Aided Design 12
- Media Technology 21
- Geography, Planning and Development 10
Countries citing papers authored by Timo Lüddecke
This map shows the geographic impact of Timo Lüddecke'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 Timo Lüddecke with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Timo Lüddecke more than expected).
Fields of papers citing papers by Timo Lüddecke
This network shows the impact of papers produced by Timo Lüddecke. 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 Timo Lüddecke. The network helps show where Timo Lüddecke may publish in the future.
Co-authors
The 25 scholars most cited alongside Timo Lüddecke, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Image Segmentation Using Text and Image Prompts Hit paper breakdown → | 2022 | 271 |
| 2 | 2017 | 15 | |
| 3 | 2019 | 15 | |
| 4 | 2019 | 8 | |
| 5 | 2021 | 7 | |
| 6 | 2020 | 7 | |
| 7 | 2019 | 7 | |
| 8 | 2024 | 6 | |
| 9 | 2025 | 4 | |
| 10 | 2025 | 4 | |
| 11 | 2020 | 2 | |
| 12 | 2023 | 1 | |
| 13 | 2025 | 1 | |
| 14 | 2017 | 0 |
About Timo Lüddecke
Timo Lüddecke is a scholar working on Computer Vision and Pattern Recognition, Control and Systems Engineering, Biomedical Engineering, Automotive Engineering and Civil and Structural Engineering, having authored 14 papers that have together received 348 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (4 papers), Multimodal Machine Learning Applications (3 papers), Robot Manipulation and Learning (3 papers), Robotic Path Planning Algorithms (2 papers), Robotic Locomotion and Control (2 papers), Advanced Neural Network Applications (2 papers), Advanced Image and Video Retrieval Techniques (2 papers) and Infrastructure Maintenance and Monitoring (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (219 citations), Artificial Intelligence (111 citations), Computer Graphics and Computer-Aided Design (12 citations), Media Technology (21 citations) and Geography, Planning and Development (10 citations). Timo Lüddecke has collaborated with scholars based in Germany, Lithuania and Hong Kong. Frequent co-authors include Alexander S. Ecker, Florentin Wörgötter, Tomas Kulvičius, Minija Tamošiūnaitė, Alejandro Agostini, Michael Fauth, Kai Han, Samuel Albanie, Jonathan C. Roberts and Nils Nölke. Their work appears in journals such as Robotics and Autonomous Systems, Nature Methods, Frontiers in Neurorobotics, Ecological Informatics and Artificial Intelligence.
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.