Anurag Arnab
- Computer Vision and Pattern Recognition top 1%
- Artificial Intelligence top 5%
- Media Technology top 10%
- Biomedical Engineering
- Radiology, Nuclear Medicine and Imaging
- Co-authors
- Philip H. S. TorrCordelia SchmidPaul Hongsuck SeoYan ShenArsha NagraniXuehan XiongMi ZhangChen Sun
- Topics
- Multimodal Machine Learning Applications (11 papers)Human Pose and Action Recognition (9 papers)Advanced Neural Network Applications (7 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligencePattern RecognitionIEEE Signal Processing Magazine
- Partner nations
- United StatesUnited KingdomAustralia
In The Last Decade
Anurag Arnab
26 papers receiving 862 citations
Hit Papers
Peers
Comparison fields: 5 of 96
- Computer Vision and Pattern Recognition 669
- Artificial Intelligence 364
- Media Technology 46
- Biomedical Engineering 45
- Radiology, Nuclear Medicine and Imaging 38
Countries citing papers authored by Anurag Arnab
This map shows the geographic impact of Anurag Arnab'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 Anurag Arnab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anurag Arnab more than expected).
Fields of papers citing papers by Anurag Arnab
This network shows the impact of papers produced by Anurag Arnab. 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 Anurag Arnab. The network helps show where Anurag Arnab may publish in the future.
Co-authorship network of co-authors of Anurag Arnab
This figure shows the co-authorship network connecting the top 25 collaborators of Anurag Arnab. A scholar is included among the top collaborators of Anurag Arnab 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 Anurag Arnab. Anurag Arnab is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 11 | |
| 2 | 5 | |
| 3 | 31 | |
| 4 | 2 | |
| 5 | 5 | |
| 6 | 9 | |
| 7 | 1 | |
| 8 | 21 | |
| 9 | 8 | |
| 10 | 4 | |
| 11 | 106 | |
| 12 | 10 | |
| 13 | TokenLearner: Adaptive Space-Time Tokenization for Videos | 52 |
| 14 | Attention Bottlenecks for Multimodal Fusion | 0 |
| 15 | 85 | |
| 16 | Dual Graph Convolutional Network for Semantic Segmentation. | 23 |
| 17 | 81 | |
| 18 | Deep Fully-Connected Part-Based Models for Human Pose Estimation | 8 |
| 19 | Learning Arbitrary Potentials in CRFs with Gradient Descent. | 2 |
| 20 | 137 |
About Anurag Arnab
Anurag Arnab is a scholar working on Computer Vision and Pattern Recognition, Equine and Computer Graphics and Computer-Aided Design, having authored 28 papers that have together received 880 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (11 papers), Human Pose and Action Recognition (9 papers) and Advanced Neural Network Applications (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (669 citations), Artificial Intelligence (364 citations) and Media Technology (46 citations). Anurag Arnab has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include Philip H. S. Torr, Cordelia Schmid, Paul Hongsuck Seo, Yan Shen, Arsha Nagrani, Xuehan Xiong, Mi Zhang, Chen Sun, Zhichao Lu and Li Zhang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition and IEEE Signal Processing Magazine.
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.