Tomas Kulvičius

1.2k total citations
55 papers, 737 citations indexed

About

Tomas Kulvičius is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience and Control and Systems Engineering. According to data from OpenAlex, Tomas Kulvičius has authored 55 papers receiving a total of 737 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Computer Vision and Pattern Recognition, 16 papers in Cognitive Neuroscience and 14 papers in Control and Systems Engineering. Recurrent topics in Tomas Kulvičius's work include Robot Manipulation and Learning (14 papers), Neural dynamics and brain function (9 papers) and Robotic Path Planning Algorithms (9 papers). Tomas Kulvičius is often cited by papers focused on Robot Manipulation and Learning (14 papers), Neural dynamics and brain function (9 papers) and Robotic Path Planning Algorithms (9 papers). Tomas Kulvičius collaborates with scholars based in Germany, Lithuania and Sweden. Tomas Kulvičius's co-authors include Florentin Wörgötter, Minija Tamošiūnaitė, Florentin Wörgötter, Poramate Manoonpong, KeJun Ning, Bernd Porr, Tao Geng, Peter B. Marschik, Luise Poustka and Dajie Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and NeuroImage.

In The Last Decade

Tomas Kulvičius

50 papers receiving 724 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tomas Kulvičius Germany 14 239 201 158 143 140 55 737
Aleksandra Kawala‐Sterniuk Poland 16 74 0.3× 205 1.0× 380 2.4× 77 0.5× 79 0.6× 84 857
Yoshiyuki Ohmura Japan 14 286 1.2× 424 2.1× 277 1.8× 78 0.5× 58 0.4× 34 689
Shuai Cao China 19 38 0.2× 555 2.8× 339 2.1× 59 0.4× 53 0.4× 48 990
Matthew Millard Germany 14 96 0.4× 1.3k 6.4× 314 2.0× 58 0.4× 113 0.8× 38 1.9k
Huy Phan United Kingdom 20 33 0.1× 364 1.8× 1.6k 9.8× 281 2.0× 272 1.9× 70 2.4k
Gerolf Vanacker Belgium 12 87 0.4× 111 0.6× 803 5.1× 146 1.0× 43 0.3× 20 1.1k
Kyoobin Lee South Korea 18 93 0.4× 205 1.0× 308 1.9× 187 1.3× 146 1.0× 59 1.1k
Beth Jelfs United Kingdom 15 34 0.1× 383 1.9× 339 2.1× 34 0.2× 118 0.8× 47 892
Reza Tafreshi Qatar 13 63 0.3× 169 0.8× 499 3.2× 38 0.3× 61 0.4× 38 686
Ze Wang China 15 76 0.3× 84 0.4× 475 3.0× 50 0.3× 48 0.3× 44 888

Countries citing papers authored by Tomas Kulvičius

Since Specialization
Citations

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

Fields of papers citing papers by Tomas Kulvičius

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tomas Kulvičius

This figure shows the co-authorship network connecting the top 25 collaborators of Tomas Kulvičius. A scholar is included among the top collaborators of Tomas Kulvičius 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 Tomas Kulvičius. Tomas Kulvičius 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.
Kulvičius, Tomas, Dajie Zhang, Luise Poustka, et al.. (2025). Deep learning empowered sensor fusion boosts infant movement classification. Communications Medicine. 5(1). 16–16. 3 indexed citations
2.
Zhang, Dajie, Luise Poustka, Sven Bölte, et al.. (2025). Comparison of marker-less 2D image-based methods for infant pose estimation. Scientific Reports. 15(1). 12148–12148. 2 indexed citations
3.
Marschik, Peter B., Bernd Wilken, Christian P. Schaaf, et al.. (2025). Shared Environment – Different Genes: Speech-Language Development in a Pair of Dizygotic Twins with and Without MECP2 Mutation. The Journal of Genetic Psychology. 186(6). 452–467.
4.
Kulvičius, Tomas, et al.. (2023). Simulated mental imagery for robotic task planning. Frontiers in Neurorobotics. 17. 1218977–1218977. 1 indexed citations
5.
Kulvičius, Tomas, Dajie Zhang, Karin Nielsen‐Saines, et al.. (2023). Infant movement classification through pressure distribution analysis. SHILAP Revista de lepidopterología. 3(1). 112–112. 10 indexed citations
6.
Marschik, Peter B., Tomas Kulvičius, Karin Nielsen‐Saines, et al.. (2023). Open video data sharing in developmental science and clinical practice. iScience. 26(4). 106348–106348. 9 indexed citations
7.
Schulte‐Rüther, Martin, Tomas Kulvičius, Sanna Stroth, et al.. (2022). Using machine learning to improve diagnostic assessment of ASD in the light of specific differential and co‐occurring diagnoses. Journal of Child Psychology and Psychiatry. 64(1). 16–26. 16 indexed citations
8.
Marschik, Peter B., Tomas Kulvičius, Karin Nielsen‐Saines, et al.. (2022). Emerging Verbal Functions in Early Infancy: Lessons from Observational and Computational Approaches on Typical Development and Neurodevelopmental Disorders. Advances in Neurodevelopmental Disorders. 6(4). 369–388. 10 indexed citations
9.
Kulvičius, Tomas, et al.. (2021). Touching events predict human action segmentation in brain and behavior. NeuroImage. 243. 118534–118534. 5 indexed citations
10.
Kulvičius, Tomas, et al.. (2021). One-Shot Multi-Path Planning Using Fully Convolutional Networks in a Comparison to Other Algorithms. Frontiers in Neurorobotics. 14. 600984–600984. 7 indexed citations
11.
Silva, Nelson, Dajie Zhang, Tomas Kulvičius, et al.. (2021). The future of General Movement Assessment: The role of computer vision and machine learning – A scoping review. Research in Developmental Disabilities. 110. 103854–103854. 82 indexed citations
12.
Wörgötter, Florentin, et al.. (2020). Humans Predict Action using Grammar-like Structures. Scientific Reports. 10(1). 3999–3999. 3 indexed citations
13.
Nielsen, Jacob, et al.. (2017). Individualised and adaptive upper limb rehabilitation with industrial robot using dynamic movement primitives. University of Southern Denmark Research Portal (University of Southern Denmark). 8 indexed citations
14.
Kulvičius, Tomas, et al.. (2016). Induction and Consolidation of Calcium-Based Homo- and Heterosynaptic Potentiation and Depression. PLoS ONE. 11(8). e0161679–e0161679. 10 indexed citations
15.
Schoeler, Markus, Florentin Wörgötter, Tomas Kulvičius, & Jérémie Papon. (2015). Unsupervised Generation of Context-Relevant Training-Sets for Visual Object Recognition Employing Multilinguality. 13. 805–812.
16.
Stein, Simon Christoph, Florentin Wörgötter, Markus Schoeler, Jérémie Papon, & Tomas Kulvičius. (2014). Convexity based object partitioning for robot applications. 3213–3220. 41 indexed citations
17.
Ning, KeJun, Tomas Kulvičius, Minija Tamošiūnaitė, & Florentin Wörgötter. (2012). A Novel Trajectory Generation Method for Robot Control. Journal of Intelligent & Robotic Systems. 68(2). 165–184. 10 indexed citations
18.
19.
Kulvičius, Tomas, et al.. (2010). Behavioral analysis of differential hebbian learning in closed-loop systems. Biological Cybernetics. 103(4). 255–271. 9 indexed citations
20.
Tamošiūnaitė, Minija, James A. Ainge, Tomas Kulvičius, et al.. (2008). Path-finding in real and simulated rats: assessing the influence of path characteristics on navigation learning. Journal of Computational Neuroscience. 25(3). 562–582. 8 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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