Rishabh Kabra
Impact in
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- Generative Adversarial Networks and Image Synthesis
- Multimodal Machine Learning Applications
- Advanced Vision and Imaging
- Human Pose and Action Recognition
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
Papers in
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- Advanced Image and Video Retrieval Techniques 2
- Generative Adversarial Networks and Image Synthesis 2
- Human Pose and Action Recognition 1
- Multimodal Machine Learning Applications 1
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- Domain Adaptation and Few-Shot Learning 1
- Co-authors
- Daniel Zoran (2 shared papers)Alexander Lerchner (2 shared papers)Danilo Jimenez Rezende (1 shared paper)Christopher Burgess (1 shared paper)Klaus Greff (1 shared paper)Löıc Matthey (1 shared paper)Matthew Botvinick (1 shared paper)Yusuf Aytar (1 shared paper)
- Journals
- arXiv (Cornell University) (1 paper)2021 IEEE/CVF International Conference on Computer Vision (ICCV) (1 paper)
- Partner nations
- United KingdomUnited States
In The Last Decade
Rishabh Kabra
2 papers receiving 22 citations
Peers
Comparison fields: 5 of 6
- Computer Vision and Pattern Recognition 18
- Computer Graphics and Computer-Aided Design 2
- Artificial Intelligence 7
- Computational Mechanics 3
- Industrial and Manufacturing Engineering 1
Countries citing papers authored by Rishabh Kabra
This map shows the geographic impact of Rishabh Kabra'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 Rishabh Kabra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rishabh Kabra more than expected).
Fields of papers citing papers by Rishabh Kabra
This network shows the impact of papers produced by Rishabh Kabra. 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 Rishabh Kabra. The network helps show where Rishabh Kabra may publish in the future.
Co-authors
The 15 scholars most cited alongside Rishabh Kabra, 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 | Multi-Object Representation Learning with Iterative Variational Inference | 2019 | 13 |
| 2 | 2021 | 9 | |
| 3 | 2024 | 0 |
About Rishabh Kabra
Rishabh Kabra is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Infectious Diseases, Organic Chemistry and Surgery, having authored 3 papers that have together received 22 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (2 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Domain Adaptation and Few-Shot Learning (1 paper), Human Pose and Action Recognition (1 paper) and Multimodal Machine Learning Applications (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (18 citations), Computer Graphics and Computer-Aided Design (2 citations), Artificial Intelligence (7 citations), Computational Mechanics (3 citations) and Industrial and Manufacturing Engineering (1 citation). Rishabh Kabra has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Daniel Zoran, Alexander Lerchner, Danilo Jimenez Rezende, Christopher Burgess, Klaus Greff, Löıc Matthey, Matthew Botvinick, Yusuf Aytar, Thomas Kipf and Sjoerd van Steenkiste. Their work appears in journals such as arXiv (Cornell University) and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
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.