Dídac Surís
Impact in
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- Multimodal Machine Learning Applications
- Human Pose and Action Recognition
- Video Analysis and Summarization
- Advanced Image and Video Retrieval Techniques
- Signal Processing top 10%
- Music and Audio Processing
- Speech and Audio Processing
Papers in
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- Multimodal Machine Learning Applications 5
- Advanced Image and Video Retrieval Techniques 4
- Human Pose and Action Recognition 2
- Video Analysis and Summarization 2
- Advanced Vision and Imaging 1
- Generative Adversarial Networks and Image Synthesis 1
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- Music and Audio Processing 2
- Co-authors
- Carl Vondrick (5 shared papers)Sachit Menon (1 shared paper)Adrià Recasens (2 shared papers)Galen Chuang (1 shared paper)Antonio Torralba (2 shared papers)James Glass (2 shared papers)David Harwath (2 shared papers)Bryan Russell (1 shared paper)
- Journals
- International Journal of Computer Vision (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (3 papers)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesCanada
In The Last Decade
Dídac Surís
9 papers receiving 208 citations
Peers
Comparison fields: 5 of 52
- Computer Vision and Pattern Recognition 147
- Signal Processing 44
- Artificial Intelligence 92
- Health Informatics 2
- Human-Computer Interaction 5
Countries citing papers authored by Dídac Surís
This map shows the geographic impact of Dídac Surís'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 Dídac Surís with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dídac Surís more than expected).
Fields of papers citing papers by Dídac Surís
This network shows the impact of papers produced by Dídac Surís. 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 Dídac Surís. The network helps show where Dídac Surís may publish in the future.
Co-authors
The 20 scholars most cited alongside Dídac Surís, 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 | 2023 | 96 | |
| 2 | 2019 | 45 | |
| 3 | 2022 | 24 | |
| 4 | 2023 | 17 | |
| 5 | 2024 | 13 | |
| 6 | 2022 | 8 | |
| 7 | 2019 | 5 | |
| 8 | 2022 | 5 | |
| 9 | Learning to Learn Words from Narrated Video. | 2019 | 3 |
About Dídac Surís
Dídac Surís is a scholar working on Computer Vision and Pattern Recognition, Signal Processing, Artificial Intelligence, Control and Systems Engineering and Infectious Diseases, having authored 9 papers that have together received 216 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (5 papers), Advanced Image and Video Retrieval Techniques (4 papers), Human Pose and Action Recognition (2 papers), Music and Audio Processing (2 papers), Video Analysis and Summarization (2 papers), Advanced Vision and Imaging (1 paper), Human Motion and Animation (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (147 citations), Signal Processing (44 citations), Artificial Intelligence (92 citations), Health Informatics (2 citations) and Human-Computer Interaction (5 citations). Dídac Surís has collaborated with scholars based in United States and Canada. Frequent co-authors include Carl Vondrick, Sachit Menon, Adrià Recasens, Galen Chuang, Antonio Torralba, James Glass, David Harwath, Bryan Russell, Justin Salamon and Achal Dave. Their work appears in journals such as International Journal of Computer Vision, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and arXiv (Cornell University).
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.