Sagar Vaze

406 citations
6 papers · 155 · h-index 4

Impact in

    • Multimodal Machine Learning Applications
    • Advanced Neural Network Applications
    • Advanced Image and Video Retrieval Techniques
    • Domain Adaptation and Few-Shot Learning
    • Text and Document Classification Technologies
    • Natural Language Processing Techniques
    • Topic Modeling
    • Machine Learning and Data Classification

Papers in

Sagar Vaze

6 papers receiving 153 citations

Peers

Sagar Vaze
Comparison fields: 5 of 46
  • Computer Vision and Pattern Recognition 80
  • Artificial Intelligence 96
  • Health Informatics 2
  • Radiology, Nuclear Medicine and Imaging 15
  • Computer Graphics and Computer-Aided Design 2
Replace Xinzhe Li with:
Xinzhe Li China
Dmitry Molchanov Russia
Wan-Duo Kurt New Zealand
Jaehyung Kim South Korea
Jiancheng Lyu United States
Mu Cai United States
Guocheng Niu China
Bingxin Xu China
Evgeniya Ustinova Russia
Sagar Vaze relative to Xinzhe Li China Xinzhe Li's profile →
Citations per field
00.5×2.6×
Xinzhe Li · 1×
Citations per year

Countries citing papers authored by Sagar Vaze

Since Specialization
Citations

This map shows the geographic impact of Sagar Vaze'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 Sagar Vaze with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sagar Vaze more than expected).

Fields of papers citing papers by Sagar Vaze

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sagar Vaze. 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 Sagar Vaze. The network helps show where Sagar Vaze may publish in the future.

Co-authors

The 10 scholars most cited alongside Sagar Vaze, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sagar Vaze Line = papers co-authored together Sagar Vaze links everyone, so they are left out of the graph.

All Works

6 of 6 papers shown

About Sagar Vaze

Sagar Vaze is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging, Oncology and Signal Processing, having authored 6 papers that have together received 155 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (2 papers), Domain Adaptation and Few-Shot Learning (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Multimodal Machine Learning Applications (1 paper), Robotics and Sensor-Based Localization (1 paper), Adversarial Robustness in Machine Learning (1 paper), Medical Imaging and Analysis (1 paper) and Rough Sets and Fuzzy Logic (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (80 citations), Artificial Intelligence (96 citations), Health Informatics (2 citations), Radiology, Nuclear Medicine and Imaging (15 citations) and Computer Graphics and Computer-Aided Design (2 citations). Sagar Vaze has collaborated with scholars based in United Kingdom, Hong Kong and Canada. Frequent co-authors include Andrea Vedaldi, Andrew Zisserman, Weidi Xie, Ana I. L. Namburete, Ioannis Havoutis, Ingmar Posner, Nicolas Carion, Ishan Misra, Kai Han and Y K S Viswanath. Their work appears in journals such as IEEE Journal of Biomedical and Health Informatics, International Journal of Computer Vision, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and 2022 International Conference on Robotics and Automation (ICRA).

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.

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