Dmytro Okhonko
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Signal Processing
- Biomedical Engineering
- Computer Networks and Communications
- Co-authors
- Florian MetzeArmen AghajanyanLuke ZettlemoyerGargi GhoshPo-Yao HuangChristoph FeichtenhoferHu XuXilun Chen
- Topics
- Natural Language Processing Techniques (4 papers)Topic Modeling (4 papers)Multimodal Machine Learning Applications (3 papers)
- Journals
- IEEE/ACM Transactions on Audio Speech and Language ProcessingProceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Partner nations
- United StatesNetherlandsCanada
In The Last Decade
Dmytro Okhonko
4 papers receiving 298 citations
Hit Papers
Peers
Comparison fields: 5 of 41
- Computer Vision and Pattern Recognition 216
- Artificial Intelligence 187
- Signal Processing 13
- Biomedical Engineering 10
- Computer Networks and Communications 8
Countries citing papers authored by Dmytro Okhonko
This map shows the geographic impact of Dmytro Okhonko'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 Dmytro Okhonko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dmytro Okhonko more than expected).
Fields of papers citing papers by Dmytro Okhonko
This network shows the impact of papers produced by Dmytro Okhonko. 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 Dmytro Okhonko. The network helps show where Dmytro Okhonko may publish in the future.
Co-authorship network of co-authors of Dmytro Okhonko
This figure shows the co-authorship network connecting the top 25 collaborators of Dmytro Okhonko. A scholar is included among the top collaborators of Dmytro Okhonko 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 Dmytro Okhonko. Dmytro Okhonko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 45 | |
| 3 | 8 | |
| 4 | VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understandingbreakdown → | 247 |
About Dmytro Okhonko
Dmytro Okhonko is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Infectious Diseases, having authored 4 papers that have together received 307 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (4 papers), Topic Modeling (4 papers) and Multimodal Machine Learning Applications (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (216 citations), Artificial Intelligence (187 citations) and Signal Processing (13 citations). Dmytro Okhonko has collaborated with scholars based in United States, Netherlands and Canada. Frequent co-authors include Florian Metze, Armen Aghajanyan, Luke Zettlemoyer, Gargi Ghosh, Po-Yao Huang, Christoph Feichtenhofer, Hu Xu, Xilun Chen, Sonal Gupta and Scott Yih. Their work appears in journals such as IEEE/ACM Transactions on Audio Speech and Language Processing and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.