Lucas Smaira
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Signal Processing top 10%
- Cancer Research
- Biomedical Engineering
- Co-authors
- Jean-Baptiste AlayracIvan LaptevAndrew ZissermanAntoine MiechJosef ŠivicLuyu WangJoão CarreiraSander Dieleman
- Topics
- Multimodal Machine Learning Applications (3 papers)Human Pose and Action Recognition (2 papers)Speech and Audio Processing (1 paper)
- Journals
- Neural Information Processing SystemsICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Partner nations
- United KingdomUnited StatesSwitzerland
In The Last Decade
Lucas Smaira
4 papers receiving 360 citations
Hit Papers
Peers
Comparison fields: 5 of 46
- Computer Vision and Pattern Recognition 322
- Artificial Intelligence 191
- Signal Processing 46
- Cancer Research 8
- Biomedical Engineering 6
Countries citing papers authored by Lucas Smaira
This map shows the geographic impact of Lucas Smaira'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 Lucas Smaira with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lucas Smaira more than expected).
Fields of papers citing papers by Lucas Smaira
This network shows the impact of papers produced by Lucas Smaira. 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 Lucas Smaira. The network helps show where Lucas Smaira may publish in the future.
Co-authorship network of co-authors of Lucas Smaira
This figure shows the co-authorship network connecting the top 25 collaborators of Lucas Smaira. A scholar is included among the top collaborators of Lucas Smaira 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 Lucas Smaira. Lucas Smaira is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 28 | |
| 2 | 1 | |
| 3 | Self-Supervised MultiModal Versatile Networks | 7 |
| 4 | End-to-End Learning of Visual Representations From Uncurated Instructional Videosbreakdown → | 339 |
About Lucas Smaira
Lucas Smaira is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing, having authored 4 papers that have together received 375 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (3 papers), Human Pose and Action Recognition (2 papers) and Speech and Audio Processing (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (322 citations), Artificial Intelligence (191 citations) and Signal Processing (46 citations). Lucas Smaira has collaborated with scholars based in United Kingdom, United States and Switzerland. Frequent co-authors include Jean-Baptiste Alayrac, Ivan Laptev, Andrew Zisserman, Antoine Miech, Josef Šivic, Luyu Wang, João Carreira, Sander Dieleman, Adrià Recasens and Pauline Luc. Their work appears in journals such as Neural Information Processing Systems and ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
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.