Muhammad Ferjad Naeem
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Radiology, Nuclear Medicine and Imaging
- Media Technology
- Ocean Engineering
- Co-authors
- Yongqin XianMassimiliano ManciniZeynep AkataLuc Van GoolMuhammad Zeshan AfzalDidier StrickerFederico TombariFaisal Shafait
- Topics
- Multimodal Machine Learning Applications (5 papers)Domain Adaptation and Few-Shot Learning (5 papers)Handwritten Text Recognition Techniques (3 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceInternational Journal of Computer VisionNeural Computing and Applications
- Partner nations
- United StatesPakistanSwitzerland
In The Last Decade
Muhammad Ferjad Naeem
8 papers receiving 146 citations
Peers
Comparison fields: 5 of 34
- Artificial Intelligence 114
- Computer Vision and Pattern Recognition 79
- Radiology, Nuclear Medicine and Imaging 35
- Media Technology 22
- Ocean Engineering 18
Countries citing papers authored by Muhammad Ferjad Naeem
This map shows the geographic impact of Muhammad Ferjad Naeem'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 Muhammad Ferjad Naeem with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Muhammad Ferjad Naeem more than expected).
Fields of papers citing papers by Muhammad Ferjad Naeem
This network shows the impact of papers produced by Muhammad Ferjad Naeem. 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 Muhammad Ferjad Naeem. The network helps show where Muhammad Ferjad Naeem may publish in the future.
Co-authorship network of co-authors of Muhammad Ferjad Naeem
This figure shows the co-authorship network connecting the top 25 collaborators of Muhammad Ferjad Naeem. A scholar is included among the top collaborators of Muhammad Ferjad Naeem 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 Muhammad Ferjad Naeem. Muhammad Ferjad Naeem is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 4 | |
| 3 | 0 | |
| 4 | 33 | |
| 5 | 14 | |
| 6 | 19 | |
| 7 | 57 | |
| 8 | 13 | |
| 9 | 6 | |
| 10 | 5 |
About Muhammad Ferjad Naeem
Muhammad Ferjad Naeem is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Human-Computer Interaction, having authored 10 papers that have together received 151 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (5 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Handwritten Text Recognition Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (79 citations), Artificial Intelligence (114 citations) and Media Technology (22 citations). Muhammad Ferjad Naeem has collaborated with scholars based in United States, Pakistan and Switzerland. Frequent co-authors include Yongqin Xian, Massimiliano Mancini, Zeynep Akata, Luc Van Gool, Muhammad Zeshan Afzal, Didier Stricker, Federico Tombari, Faisal Shafait, Adnan Ul-Hasan and Alain Pagani. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and Neural Computing and Applications.
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.