Deepak Poddar
Impact in
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- Human Pose and Action Recognition
- Video Surveillance and Tracking Methods
- Advanced Neural Network Applications
- Advanced Vision and Imaging
- Human-Computer Interaction top 10%
- Hand Gesture Recognition Systems
Papers in
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- Advanced Neural Network Applications 3
- Video Surveillance and Tracking Methods 2
- Advanced Vision and Imaging 2
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- Autonomous Vehicle Technology and Safety 4
- Co-authors
- Soyeb Nagori (6 shared papers)Manu Mathew (6 shared papers)Debapriya Maji (2 shared papers)Mihir Mody (5 shared papers)Hrushikesh Garud (3 shared papers)Jason W. Jones (1 shared paper)Zoran Nikolić (1 shared paper)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (1 paper)Electronic Imaging (2 papers)
- Partner nations
- United StatesIndia
In The Last Decade
Deepak Poddar
10 papers receiving 237 citations
Deepak Poddar's Hit Papers
Peers
Comparison fields: 5 of 70
- Computer Vision and Pattern Recognition 135
- Human-Computer Interaction 31
- Automotive Engineering 27
- Radiological and Ultrasound Technology 9
- Industrial and Manufacturing Engineering 15
Countries citing papers authored by Deepak Poddar
This map shows the geographic impact of Deepak Poddar'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 Deepak Poddar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepak Poddar more than expected).
Fields of papers citing papers by Deepak Poddar
This network shows the impact of papers produced by Deepak Poddar. 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 Deepak Poddar. The network helps show where Deepak Poddar may publish in the future.
Co-authors
The 7 scholars most cited alongside Deepak Poddar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss Hit paper breakdown → | 2022 | 192 |
| 2 | 2017 | 14 | |
| 3 | 2015 | 13 | |
| 4 | 2016 | 13 | |
| 5 | 2018 | 6 | |
| 6 | 2019 | 4 | |
| 7 | 2024 | 1 | |
| 8 | 2017 | 1 | |
| 9 | 2012 | 1 | |
| 10 | 2020 | 1 |
About Deepak Poddar
Deepak Poddar is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering, Artificial Intelligence, Electrical and Electronic Engineering and Hardware and Architecture, having authored 10 papers that have together received 246 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (4 papers), Advanced Neural Network Applications (3 papers), Video Surveillance and Tracking Methods (2 papers), CCD and CMOS Imaging Sensors (2 papers), Advanced Vision and Imaging (2 papers), Adversarial Robustness in Machine Learning (2 papers), Vehicular Ad Hoc Networks (VANETs) (1 paper) and Robotics and Sensor-Based Localization (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (135 citations), Human-Computer Interaction (31 citations), Automotive Engineering (27 citations), Radiological and Ultrasound Technology (9 citations) and Industrial and Manufacturing Engineering (15 citations). Deepak Poddar has collaborated with scholars based in United States and India. Frequent co-authors include Soyeb Nagori, Manu Mathew, Debapriya Maji, Mihir Mody, Hrushikesh Garud, Jason W. Jones and Zoran Nikolić. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) and Electronic Imaging.
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.