Pavan Kumar Anasosalu Vasu
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Industrial and Manufacturing Engineering
- Aerospace Engineering
- Electrical and Electronic Engineering
- Co-authors
- Oncel TuzelJeff ZhuAnurag RanjanJames GabrielHadi PouransariRaviteja VemulapalliFartash FaghriSachin Mehta
- Topics
- Advanced Image and Video Retrieval Techniques (4 papers)Advanced Neural Network Applications (3 papers)Multimodal Machine Learning Applications (3 papers)
- Cited by
- Computer Vision and Pattern RecognitionIndustrial and Manufacturing EngineeringMedia Technology
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Partner nations
- United States
In The Last Decade
Pavan Kumar Anasosalu Vasu
6 papers receiving 240 citations
Hit Papers
Peers
Comparison fields: 5 of 64
- Computer Vision and Pattern Recognition 129
- Artificial Intelligence 54
- Industrial and Manufacturing Engineering 25
- Aerospace Engineering 25
- Electrical and Electronic Engineering 23
Countries citing papers authored by Pavan Kumar Anasosalu Vasu
This map shows the geographic impact of Pavan Kumar Anasosalu Vasu'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 Pavan Kumar Anasosalu Vasu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pavan Kumar Anasosalu Vasu more than expected).
Fields of papers citing papers by Pavan Kumar Anasosalu Vasu
This network shows the impact of papers produced by Pavan Kumar Anasosalu Vasu. 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 Pavan Kumar Anasosalu Vasu. The network helps show where Pavan Kumar Anasosalu Vasu may publish in the future.
Co-authorship network of co-authors of Pavan Kumar Anasosalu Vasu
This figure shows the co-authorship network connecting the top 25 collaborators of Pavan Kumar Anasosalu Vasu. A scholar is included among the top collaborators of Pavan Kumar Anasosalu Vasu 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 Pavan Kumar Anasosalu Vasu. Pavan Kumar Anasosalu Vasu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 32 | |
| 3 | 11 | |
| 4 | MobileOne: An Improved One millisecond Mobile Backbonebreakdown → | 145 |
| 5 | 53 | |
| 6 | 8 |
About Pavan Kumar Anasosalu Vasu
Pavan Kumar Anasosalu Vasu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications, having authored 6 papers that have together received 250 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (4 papers), Advanced Neural Network Applications (3 papers) and Multimodal Machine Learning Applications (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (129 citations), Industrial and Manufacturing Engineering (25 citations) and Media Technology (19 citations). Pavan Kumar Anasosalu Vasu has collaborated with scholars based in United States. Frequent co-authors include Oncel Tuzel, Jeff Zhu, Anurag Ranjan, James Gabriel, Hadi Pouransari, Raviteja Vemulapalli, Fartash Faghri, Sachin Mehta, Mohammad Rastegari and Haoxiang Wang. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
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.