Deva Ramanan
Impact in
- Computer Vision and Pattern Recognition top 0.01%
- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Advanced Image and Video Retrieval Techniques
- Human Pose and Action Recognition
- Multimodal Machine Learning Applications
- Advanced Vision and Imaging
- Artificial Intelligence top 0.02%
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
Papers in
-
- Human Pose and Action Recognition 47
- Advanced Image and Video Retrieval Techniques 39
- Advanced Neural Network Applications 37
- Video Surveillance and Tracking Methods 37
- Advanced Vision and Imaging 32
-
- Domain Adaptation and Few-Shot Learning 21
- Anomaly Detection Techniques and Applications 19
- Co-authors
- James Hays (8 shared papers)Piotr Dollár (2 shared papers)C. Lawrence Zitnick (2 shared papers)Pietro Perona (1 shared paper)Tsung-Yi Lin (1 shared paper)Michael Maire (1 shared paper)Serge Belongie (1 shared paper)Pedro F. Felzenszwalb (5 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (7 papers)International Journal of Computer Vision (5 papers)IEEE Robotics and Automation Letters (2 papers)Communications of the ACM (2 papers)Lecture notes in computer science (16 papers)
- Partner nations
- United StatesGermanyUnited Kingdom
In The Last Decade
Deva Ramanan
164 papers receiving 51.9k citations
Deva Ramanan's Hit Papers
Peers
Comparison fields: 5 of 210
- Computer Vision and Pattern Recognition 43.4k
- Artificial Intelligence 14.0k
- Human-Computer Interaction 2.4k
- Media Technology 3.4k
- Computer Graphics and Computer-Aided Design 1.0k
Countries citing papers authored by Deva Ramanan
This map shows the geographic impact of Deva Ramanan'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 Deva Ramanan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deva Ramanan more than expected).
Fields of papers citing papers by Deva Ramanan
This network shows the impact of papers produced by Deva Ramanan. 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 Deva Ramanan. The network helps show where Deva Ramanan may publish in the future.
Co-authors
The 25 scholars most cited alongside Deva Ramanan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 170 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Microsoft COCO: Common Objects in Context Hit paper breakdown → | 2014 | 28317 |
| 2 | Object Detection with Discriminatively Trained Part-Based Models Hit paper breakdown → | 2009 | 7492 |
| 3 | A discriminatively trained, multiscale, deformable part model Hit paper breakdown → | 2008 | 2217 |
| 4 | Face detection, pose estimation, and landmark localization in the wild Hit paper breakdown → | 2012 | 1628 |
| 5 | Argoverse: 3D Tracking and Forecasting With Rich Maps Hit paper breakdown → | 2019 | 991 |
| 6 | Articulated pose estimation with flexible mixtures-of-parts Hit paper breakdown → | 2011 | 789 |
| 7 | Globally-optimal greedy algorithms for tracking a variable number of objects Hit paper breakdown → | 2011 | 618 |
| 8 | Articulated Human Detection with Flexible Mixtures of Parts Hit paper breakdown → | 2012 | 586 |
| 9 | Depth-supervised NeRF: Fewer Views and Faster Training for Free Hit paper breakdown → | 2022 | 545 |
| 10 | Finding Tiny Faces Hit paper breakdown → | 2017 | 540 |
| 11 | Detecting activities of daily living in first-person camera views Hit paper breakdown → | 2012 | 507 |
| 12 | Need for Speed: A Benchmark for Higher Frame Rate Object Tracking Hit paper breakdown → | 2017 | 368 |
| 13 | Efficiently Scaling up Crowdsourced Video Annotation Hit paper breakdown → | 2012 | 351 |
| 14 | 2007 | 333 | |
| 15 | 2015 | 273 | |
| 16 | 2006 | 267 | |
| 17 | 2013 | 243 | |
| 18 | 2019 | 240 | |
| 19 | Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly- Throughs Hit paper breakdown → | 2022 | 237 |
| 20 | 2005 | 232 |
About Deva Ramanan
Deva Ramanan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Aerospace Engineering, Computational Mechanics and Automotive Engineering, having authored 170 papers that have together received 53.8k indexed citations. Recurring topics across this work include Human Pose and Action Recognition (47 papers), Advanced Image and Video Retrieval Techniques (39 papers), Advanced Neural Network Applications (37 papers), Video Surveillance and Tracking Methods (37 papers), Advanced Vision and Imaging (32 papers), Domain Adaptation and Few-Shot Learning (21 papers), Anomaly Detection Techniques and Applications (19 papers) and Robotics and Sensor-Based Localization (19 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (43.4k citations), Artificial Intelligence (14.0k citations), Human-Computer Interaction (2.4k citations), Media Technology (3.4k citations) and Computer Graphics and Computer-Aided Design (1.0k citations). Deva Ramanan has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include James Hays, Piotr Dollár, C. Lawrence Zitnick, Pietro Perona, Tsung-Yi Lin, Michael Maire, Serge Belongie, Pedro F. Felzenszwalb, David McAllester and Ross Girshick. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, IEEE Robotics and Automation Letters, Communications of the ACM and Lecture notes in computer science.
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.