Rowan Zellers
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 5%
- Experimental and Cognitive Psychology top 5%
- Signal Processing top 5%
- Information Systems
- Co-authors
- Eli PincusAmir ZadehLouis–Philippe MorencyYejin ChoiAli FarhadiAri HoltzmanYonatan BiskXiming Lu
- Topics
- Topic Modeling (9 papers)Natural Language Processing Techniques (7 papers)Multimodal Machine Learning Applications (4 papers)
- Journals
- IEEE Intelligent Systems2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)arXiv (Cornell University)
- Partner nations
- United StatesUnited KingdomSouth Korea
In The Last Decade
Rowan Zellers
13 papers receiving 879 citations
Hit Papers
Peers
Comparison fields: 5 of 78
- Artificial Intelligence 729
- Computer Vision and Pattern Recognition 257
- Experimental and Cognitive Psychology 191
- Signal Processing 103
- Information Systems 44
Countries citing papers authored by Rowan Zellers
This map shows the geographic impact of Rowan Zellers'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 Rowan Zellers with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rowan Zellers more than expected).
Fields of papers citing papers by Rowan Zellers
This network shows the impact of papers produced by Rowan Zellers. 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 Rowan Zellers. The network helps show where Rowan Zellers may publish in the future.
Co-authorship network of co-authors of Rowan Zellers
This figure shows the co-authorship network connecting the top 25 collaborators of Rowan Zellers. A scholar is included among the top collaborators of Rowan Zellers 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 Rowan Zellers. Rowan Zellers is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 16 | |
| 2 | 9 | |
| 3 | 30 | |
| 4 | 6 | |
| 5 | 9 | |
| 6 | MAUVE: Human-Machine Divergence Curves for Evaluating Open-Ended Text Generation. | 1 |
| 7 | 39 | |
| 8 | 7 | |
| 9 | 11 | |
| 10 | Evaluating Machines by their Real-World Language Use | 5 |
| 11 | Probing Text Models for Common Ground with Visual Representations | 5 |
| 12 | HellaSwag: Can a Machine Really Finish Your Sentence?breakdown → | 332 |
| 13 | Multimodal Sentiment Intensity Analysis in Videos: Facial Gestures and Verbal Messagesbreakdown → | 441 |
About Rowan Zellers
Rowan Zellers is a scholar working on Music, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 13 papers that have together received 911 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (7 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Artificial Intelligence (729 citations), Experimental and Cognitive Psychology (191 citations) and Health Informatics (18 citations). Rowan Zellers has collaborated with scholars based in United States, United Kingdom and South Korea. Frequent co-authors include Eli Pincus, Amir Zadeh, Louis–Philippe Morency, Yejin Choi, Ali Farhadi, Ari Holtzman, Yonatan Bisk, Ximing Lu, Ronan Le Bras and Peter West. Their work appears in journals such as IEEE Intelligent Systems, 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.