Eric Kolve
Impact in
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- Multimodal Machine Learning Applications
- Advanced Image and Video Retrieval Techniques
- Data Visualization and Analytics
- Video Analysis and Summarization
- Human Pose and Action Recognition
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- Topic Modeling
- Natural Language Processing Techniques
- Domain Adaptation and Few-Shot Learning
Papers in
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- Multimodal Machine Learning Applications 4
- Human Pose and Action Recognition 2
- Video Analysis and Summarization 1
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- Topic Modeling 2
- Reinforcement Learning in Robotics 1
- Co-authors
- Aniruddha Kembhavi (5 shared papers)Minjoon Seo (1 shared paper)Ali Farhadi (3 shared papers)Hannaneh Hajishirzi (2 shared papers)Kiana Ehsani (2 shared papers)Luca Weihs (1 shared paper)Roozbeh Mottaghi (2 shared papers)Dustin Schwenk (2 shared papers)
- Journals
- Lecture notes in computer science (2 papers)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesSouth Korea
In The Last Decade
Eric Kolve
5 papers receiving 127 citations
Peers
Comparison fields: 5 of 38
- Computer Vision and Pattern Recognition 94
- Artificial Intelligence 74
- Computer Graphics and Computer-Aided Design 3
- Health Informatics 1
- Biophysics 3
Countries citing papers authored by Eric Kolve
This map shows the geographic impact of Eric Kolve'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 Eric Kolve with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Eric Kolve more than expected).
Fields of papers citing papers by Eric Kolve
This network shows the impact of papers produced by Eric Kolve. 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 Eric Kolve. The network helps show where Eric Kolve may publish in the future.
Co-authors
The 21 scholars most cited alongside Eric Kolve, 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 | 2016 | 117 | |
| 2 | 2019 | 6 | |
| 3 | 2022 | 4 | |
| 4 | 2021 | 3 | |
| 5 | 2022 | 2 |
About Eric Kolve
Eric Kolve is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Infectious Diseases, Organic Chemistry and Surgery, having authored 5 papers that have together received 132 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (4 papers), Topic Modeling (2 papers), Human Pose and Action Recognition (2 papers), Video Analysis and Summarization (1 paper) and Reinforcement Learning in Robotics (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (94 citations), Artificial Intelligence (74 citations), Computer Graphics and Computer-Aided Design (3 citations), Health Informatics (1 citation) and Biophysics (3 citations). Eric Kolve has collaborated with scholars based in United States and South Korea. Frequent co-authors include Aniruddha Kembhavi, Minjoon Seo, Ali Farhadi, Hannaneh Hajishirzi, Kiana Ehsani, Luca Weihs, Roozbeh Mottaghi, Dustin Schwenk, Christopher Clark and Sam Skjonsberg. Their work appears in journals such as Lecture notes in computer science, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing 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.