Aseem Behl
- Computer Vision and Pattern Recognition top 5%
- Automotive Engineering top 5%
- Artificial Intelligence top 10%
- Aerospace Engineering
- Control and Systems Engineering
- Co-authors
- Andreas GeigerJoel JanaiFatma GüneyEshed Ohn-BarKashyap ChittaCarsten RotherSiva Karthik MustikovelaOmid Hosseini Jafari
- Topics
- Advanced Neural Network Applications (5 papers)Advanced Image and Video Retrieval Techniques (2 papers)Autonomous Vehicle Technology and Safety (2 papers)
- Journals
- PubMedarXiv (Cornell University)
- Partner nations
- GermanyIndiaSwitzerland
In The Last Decade
Aseem Behl
8 papers receiving 504 citations
Hit Papers
Peers
Comparison fields: 5 of 72
- Computer Vision and Pattern Recognition 328
- Automotive Engineering 148
- Artificial Intelligence 125
- Aerospace Engineering 80
- Control and Systems Engineering 43
Countries citing papers authored by Aseem Behl
This map shows the geographic impact of Aseem Behl'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 Aseem Behl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aseem Behl more than expected).
Fields of papers citing papers by Aseem Behl
This network shows the impact of papers produced by Aseem Behl. 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 Aseem Behl. The network helps show where Aseem Behl may publish in the future.
Co-authorship network of co-authors of Aseem Behl
This figure shows the co-authorship network connecting the top 25 collaborators of Aseem Behl. A scholar is included among the top collaborators of Aseem Behl 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 Aseem Behl. Aseem Behl is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Computer Vision for Autonomous Vehiclesbreakdown → | 341 |
| 2 | 46 | |
| 3 | 35 | |
| 4 | PointFlowNet: Learning Representations for 3D Scene Flow Estimation from Point Clouds. | 6 |
| 5 | 81 | |
| 6 | A Corpus Linguistic Study of Bollywood Song Lyrics in the Framework of Complex Network Theory | 1 |
| 7 | 5 | |
| 8 | 4 |
About Aseem Behl
Aseem Behl is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design and Automotive Engineering, having authored 8 papers that have together received 519 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (5 papers), Advanced Image and Video Retrieval Techniques (2 papers) and Autonomous Vehicle Technology and Safety (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (328 citations), Automotive Engineering (148 citations) and Media Technology (38 citations). Aseem Behl has collaborated with scholars based in Germany, India and Switzerland. Frequent co-authors include Andreas Geiger, Joel Janai, Fatma Güney, Eshed Ohn-Bar, Kashyap Chitta, Carsten Rother, Siva Karthik Mustikovela, Omid Hosseini Jafari, Hassan Abu Alhaija and Aditya Prakash. Their work appears in journals such as PubMed 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.