Baldo Faieta
Impact in
-
- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Human Pose and Action Recognition
- Generative Adversarial Networks and Image Synthesis
-
- Domain Adaptation and Few-Shot Learning
- Topic Modeling
- Natural Language Processing Techniques
- Sentiment Analysis and Opinion Mining
Papers in
-
- Advanced Image and Video Retrieval Techniques 1
- Image Retrieval and Classification Techniques 1
-
- Domain Adaptation and Few-Shot Learning 1
- Co-authors
- Zhe Lin (2 shared papers)Jason Kuen (1 shared paper)Jianming Zhang (1 shared paper)Yilin Wang (1 shared paper)Xin Yuan (1 shared paper)Ajinkya Kale (1 shared paper)Michael Maire (1 shared paper)Erik D. Lumer (1 shared paper)
- Journals
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)
- Partner nations
- United StatesBelgiumUnited Kingdom
In The Last Decade
Baldo Faieta
4 papers receiving 139 citations
Peers
Comparison fields: 5 of 50
- Computer Vision and Pattern Recognition 67
- Artificial Intelligence 78
- Human-Computer Interaction 9
- Computer Graphics and Computer-Aided Design 4
- Health Informatics 1
Countries citing papers authored by Baldo Faieta
This map shows the geographic impact of Baldo Faieta'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 Baldo Faieta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Baldo Faieta more than expected).
Fields of papers citing papers by Baldo Faieta
This network shows the impact of papers produced by Baldo Faieta. 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 Baldo Faieta. The network helps show where Baldo Faieta may publish in the future.
Co-authors
The 14 scholars most cited alongside Baldo Faieta, 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 | 2021 | 102 | |
| 2 | 2021 | 19 | |
| 3 | Exploratory database analysis via self-organization | 1994 | 11 |
| 4 | 2006 | 9 |
About Baldo Faieta
Baldo Faieta is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Communication, Computer Networks and Communications and Social Psychology, having authored 4 papers that have together received 141 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (1 paper), Team Dynamics and Performance (1 paper), Advanced Image and Video Retrieval Techniques (1 paper), Data Management and Algorithms (1 paper), Knowledge Management and Sharing (1 paper), Image Retrieval and Classification Techniques (1 paper), Advanced Database Systems and Queries (1 paper) and Innovative Teaching and Learning Methods (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (67 citations), Artificial Intelligence (78 citations), Human-Computer Interaction (9 citations), Computer Graphics and Computer-Aided Design (4 citations) and Health Informatics (1 citation). Baldo Faieta has collaborated with scholars based in United States, Belgium and United Kingdom. Frequent co-authors include Zhe Lin, Jason Kuen, Jianming Zhang, Yilin Wang, Xin Yuan, Ajinkya Kale, Michael Maire, Erik D. Lumer, Bernardo A. Huberman and Paul Verhaeghe. Their work appears in journals such as 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.