Gemma Roig

2.3k total citations
51 papers, 864 citations indexed

About

Gemma Roig is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Gemma Roig has authored 51 papers receiving a total of 864 indexed citations (citations by other indexed papers that have themselves been cited), including 31 papers in Computer Vision and Pattern Recognition, 15 papers in Cognitive Neuroscience and 13 papers in Artificial Intelligence. Recurrent topics in Gemma Roig's work include Advanced Image and Video Retrieval Techniques (11 papers), Visual Attention and Saliency Detection (10 papers) and Visual perception and processing mechanisms (8 papers). Gemma Roig is often cited by papers focused on Advanced Image and Video Retrieval Techniques (11 papers), Visual Attention and Saliency Detection (10 papers) and Visual perception and processing mechanisms (8 papers). Gemma Roig collaborates with scholars based in Germany, United States and Singapore. Gemma Roig's co-authors include Xavier Boix, Luc Van Gool, Michael Van den Bergh, Kshitij Dwivedi, J. de Curtò, Carlos T. Calafate, I. de Zarzà, Radoslaw Martin Cichy, Ngai‐Man Cheung and Yuval Elovici and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and NeuroImage.

In The Last Decade

Gemma Roig

45 papers receiving 836 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gemma Roig Germany 15 412 246 140 100 47 51 864
Christopher Olah United States 3 845 2.1× 497 2.0× 89 0.6× 84 0.8× 103 2.2× 3 1.3k
Hyeran Byun South Korea 20 861 2.1× 444 1.8× 225 1.6× 136 1.4× 141 3.0× 137 1.6k
Robert Geirhos Germany 5 410 1.0× 623 2.5× 151 1.1× 36 0.4× 43 0.9× 12 1.2k
Claudio Michaelis Germany 4 387 0.9× 605 2.5× 122 0.9× 34 0.3× 42 0.9× 4 1.1k
Aparecido Nilceu Marana Brazil 15 640 1.6× 395 1.6× 64 0.5× 48 0.5× 185 3.9× 58 1.0k
Mohamad Ivan Fanany Indonesia 16 254 0.6× 464 1.9× 116 0.8× 54 0.5× 92 2.0× 89 1.0k
Novi Quadrianto United Kingdom 15 320 0.8× 473 1.9× 249 1.8× 26 0.3× 180 3.8× 40 1.0k
Piotr Mardziel United States 8 322 0.8× 520 2.1× 27 0.2× 43 0.4× 67 1.4× 12 906
Joseph Lemley Ireland 12 315 0.8× 238 1.0× 79 0.6× 43 0.4× 68 1.4× 43 803
Tom Duerig United States 6 1.1k 2.7× 631 2.6× 55 0.4× 87 0.9× 49 1.0× 6 1.5k

Countries citing papers authored by Gemma Roig

Since Specialization
Citations

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

Fields of papers citing papers by Gemma Roig

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gemma Roig

This figure shows the co-authorship network connecting the top 25 collaborators of Gemma Roig. A scholar is included among the top collaborators of Gemma Roig 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 Gemma Roig. Gemma Roig 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.
Vilas, Martina G., et al.. (2025). Net2Brain: a toolbox to compare artificial vision models with human brain responses. Frontiers in Neuroinformatics. 19. 1515873–1515873.
2.
Christensen, Julia F., et al.. (2024). EMOKINE: A software package and computational framework for scaling up the creation of highly controlled emotional full-body movement datasets. Behavior Research Methods. 56(7). 7498–7542. 2 indexed citations
3.
Curtò, J. de, I. de Zarzà, Gemma Roig, & Carlos T. Calafate. (2024). Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis. SHILAP Revista de lepidopterología. 5(2). 181–201. 6 indexed citations
4.
Dwivedi, Kshitij, Polina Iamshchinina, Gemma Roig, et al.. (2024). Modeling short visual events through the BOLD moments video fMRI dataset and metadata. Nature Communications. 15(1). 6241–6241. 10 indexed citations
5.
Zarzà, I. de, J. de Curtò, Gemma Roig, Pietro Manzoni, & Carlos T. Calafate. (2023). Emergent Cooperation and Strategy Adaptation in Multi-Agent Systems: An Extended Coevolutionary Theory with LLMs. Electronics. 12(12). 2722–2722. 19 indexed citations
6.
Curtò, J. de, I. de Zarzà, Gemma Roig, & Carlos T. Calafate. (2023). Signature and Log-Signature for the Study of Empirical Distributions Generated with GANs. MDPI (MDPI AG). 1 indexed citations
7.
Curtò, J. de, I. de Zarzà, Gemma Roig, et al.. (2023). LLM-Informed Multi-Armed Bandit Strategies for Non-Stationary Environments. Electronics. 12(13). 2814–2814. 16 indexed citations
8.
Zarzà, I. de, J. de Curtò, Gemma Roig, & Carlos T. Calafate. (2023). LLM Multimodal Traffic Accident Forecasting. Sensors. 23(22). 9225–9225. 48 indexed citations
9.
Zarzà, I. de, J. de Curtò, Gemma Roig, & Carlos T. Calafate. (2023). Optimized Financial Planning: Integrating Individual and Cooperative Budgeting Models with LLM Recommendations. SHILAP Revista de lepidopterología. 5(1). 91–114. 14 indexed citations
10.
Curtò, J. de, I. de Zarzà, Gemma Roig, & Carlos T. Calafate. (2023). Summarization of Videos with the Signature Transform. Electronics. 12(7). 1735–1735. 7 indexed citations
11.
Zarzà, I. de, J. de Curtò, Gemma Roig, & Carlos T. Calafate. (2023). LLM Adaptive PID Control for B5G Truck Platooning Systems. Sensors. 23(13). 5899–5899. 12 indexed citations
12.
Dwivedi, Kshitij, et al.. (2022). A large and rich EEG dataset for modeling human visual object recognition. NeuroImage. 264. 119754–119754. 39 indexed citations
13.
Dwivedi, Kshitij, et al.. (2022). The spatiotemporal neural dynamics of object location representations in the human brain. Nature Human Behaviour. 6(6). 796–811. 22 indexed citations
14.
Dwivedi, Kshitij, Michael Bonner, Radoslaw Martin Cichy, & Gemma Roig. (2021). Unveiling functions of the visual cortex using task-specific deep neural networks. PLoS Computational Biology. 17(8). e1009267–e1009267. 27 indexed citations
15.
Dwivedi, Kshitij, Radoslaw Martin Cichy, & Gemma Roig. (2020). Unraveling Representations in Scene-selective Brain Regions Using Scene-Parsing Deep Neural Networks. Journal of Cognitive Neuroscience. 33(10). 2032–2043. 10 indexed citations
16.
Roig, Gemma, et al.. (2019). Multiphase and Multivariable Linear Controllers That Account for the Joint Torques in Normal Human Walking. IEEE Transactions on Biomedical Engineering. 67(6). 1573–1584. 1 indexed citations
17.
Roig, Gemma, et al.. (2017). Is the Human Visual System Invariant to Translation and Scale. National Conference on Artificial Intelligence. 6 indexed citations
18.
Roig, Gemma, et al.. (2017). Eccentricity Dependent Deep Neural Networks: Modeling Invariance in Human Vision. DSpace@MIT (Massachusetts Institute of Technology). 9 indexed citations
19.
Roig, Gemma, et al.. (2017). Eccentricity Dependent Deep Neural Networks for Modeling Human Vision. Journal of Vision. 17(10). 808–808. 2 indexed citations
20.
Roig, Gemma, Xavier Boix, & Fernando De la Torre. (2009). Optimal feature selection for subspace image matching. 200–205. 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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