José Oramas

1.2k total citations · 1 hit paper
32 papers, 719 citations indexed

About

José Oramas is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Developmental and Educational Psychology. According to data from OpenAlex, José Oramas has authored 32 papers receiving a total of 719 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Computer Vision and Pattern Recognition, 14 papers in Artificial Intelligence and 3 papers in Developmental and Educational Psychology. Recurrent topics in José Oramas's work include Human Pose and Action Recognition (8 papers), Advanced Neural Network Applications (7 papers) and Explainable Artificial Intelligence (XAI) (6 papers). José Oramas is often cited by papers focused on Human Pose and Action Recognition (8 papers), Advanced Neural Network Applications (7 papers) and Explainable Artificial Intelligence (XAI) (6 papers). José Oramas collaborates with scholars based in Belgium, Netherlands and France. José Oramas's co-authors include Tinne Tuytelaars, Basura Fernando, Efstratios Gavves, Amir Ghodrati, Florian Arendt, Sebastian Scherr, Thomas Frissen, Fien Depaepe, Annelies Raes and Pieter Vanneste and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Remote Sensing and Neurocomputing.

In The Last Decade

José Oramas

24 papers receiving 698 citations

Hit Papers

align trajectories

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.

Modeling video evolution for action recognition

2015 381 citations Basura Fernando, Efstratios Gavves et al. Lirias (KU Leuven) profile →

Peers

José Oramas

CVPR

AI

BE

HI

SP

Nazlı İkizler-Cinbiş Türkiye

Noriko Takemura Japan

Kiwon Yun United States

Hyeokhyen Kwon United States

Dan Mikami Japan

José Ramón Padilla-López Spain

Hanbo Wu China

Md. Kamrul Hasan Bangladesh

Holger Junker Switzerland

Haocong Rao China

Nazlı İkizler-Cinbiş Türkiye

José Oramas

631 ×1.0

CVPR

365 ×1.1

AI

70 ×0.3

BE

68 ×0.5

HI

28 ×1.0

SP

Citations per year, relative to José Oramas José Oramas (= 1×) peers Nazlı İkizler-Cinbiş

Countries citing papers authored by José Oramas

Since Specialization

Citations

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

Fields of papers citing papers by José Oramas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of José Oramas

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

All Works

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20 of 20 papers shown

1.

Oramas, José, et al.. (2025). A beginner’s approach to deep learning applied to VS and MD techniques. Journal of Cheminformatics. 17(1). 47–47.

2.

Oramas, José, et al.. (2024). Towards the characterization of representations learned via capsule-based network architectures. Neurocomputing. 617. 129027–129027.

3.

Oramas, José, et al.. (2024). On the coherency of quantitative evaluation of visual explanations. Computer Vision and Image Understanding. 241. 103934–103934. 1 indexed citations

4.

Steckel, Jan, et al.. (2024). Deep Learning Model Compression for Resource Efficient Activity Recognition on Edge Devices: A Case Study. Institutional Repository University of Antwerp (University of Antwerp). 575–584. 1 indexed citations

5.

Oramas, José, et al.. (2023). Training Methods of Multi-Label Prediction Classifiers for Hyperspectral Remote Sensing Images. Remote Sensing. 15(24). 5656–5656.

6.

Oramas, José, et al.. (2023). Training Methods of Multi-Label Prediction Classifiers for Hyperspectral Remote Sensing Images. SSRN Electronic Journal.

7.

Mets, Kevin, et al.. (2023). Human Motion Prediction on the IKEA-ASM Dataset. 906–914.

8.

Oramas, José, et al.. (2023). Interpreting Convolutional Neural Networks by Explaining Their Predictions. 32. 1685–1689. 2 indexed citations

9.

Oramas, José, et al.. (2023). A Protocol for Evaluating Model Interpretation Methods from Visual Explanations. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 1421–1429.

10.

Haver, Annemieke Van, et al.. (2023). Automated Virtual Reduction of Displaced Distal Radius Fractures. Institutional Repository University of Antwerp (University of Antwerp). 1–4. 2 indexed citations

11.

Mets, Kevin, et al.. (2022). Object Detection To Enable Autonomous Vessels On European Inland Waterways. IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society. 1–6.

12.

Bibal, Adrien, et al.. (2022). AIMLAI: Advances in Interpretable Machine Learning and Artificial Intelligence. Proceedings of the 31st ACM International Conference on Information & Knowledge Management. 5160–5160. 1 indexed citations

13.

Vanneste, Pieter, José Oramas, Tinne Tuytelaars, et al.. (2021). Computer Vision and Human Behaviour, Emotion and Cognition Detection: A Use Case on Student Engagement. Mathematics. 9(3). 287–287. 39 indexed citations

14.

Wang, Kaili, Liqian Ma, José Oramas, Luc Van Gool, & Tinne Tuytelaars. (2018). Integrated unpaired appearance-preserving shape translation across domains.. arXiv (Cornell University). 2 indexed citations

15.

Brabandere, Bert De, et al.. (2018). From Pixels to Actions: Learning to Drive a Car with Deep Neural Networks. Lirias (KU Leuven). 606–615. 3 indexed citations

16.

Oramas, José, et al.. (2015). Towards sign language recognition based on body parts relations. Lirias (KU Leuven). 2454–2458. 10 indexed citations

17.

Fernando, Basura, Efstratios Gavves, José Oramas, Amir Ghodrati, & Tinne Tuytelaars. (2015). Modeling video evolution for action recognition. Lirias (KU Leuven). 5378–5387. 381 indexed citations breakdown →

18.

Oramas, José, Luc De Raedt, & Tinne Tuytelaars. (2014). Towards cautious collective inference for object verification. Lirias (KU Leuven). 269–276. 1 indexed citations

19.

Oramas, José, Luc De Raedt, & Tinne Tuytelaars. (2013). Allocentric Pose Estimation. Lirias (KU Leuven). 289–296. 3 indexed citations

20.

Otterlo, Martijn van, et al.. (2012). A RELATIONAL DISTANCE-BASED FRAMEWORK FOR HIERARCHICAL IMAGE UNDERSTANDING. Lirias (KU Leuven). 206–218. 3 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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