Andrew Zisserman: h-index, Total Citations, and Citation Map
Andrew Zisserman's h-index is 210 (716 i10-index, 515,292+ total citations across 162+ publications) according to Google Scholar as of June 2026. Andrew Zisserman is affiliated with University of Oxford.
Andrew Zisserman is a researcher affiliated with University of Oxford, specializing in Computer Vision, Machine Learning. Their work has been cited 515,292 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Andrew Zisserman's Citation Metrics
Bibliometric impact based on 162 indexed publications.
- H-Index
- 210
- i10-Index
- 716
- Total Citations
- 515,292
- Citing Countries
- 35
As of June 2026.
Andrew Zisserman has an h-index of 210 and 515,292 total citations across 162 publications, with research cited by institutions in 35 countries.
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We've mapped 5,000 of 515,292 citations for Andrew Zisserman
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Global Impact Map
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Very deep convolutional networks for large-scale image recognition
2014163,369
Top Citing Countries
Top Citing Institutions
Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
6 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher advanced large-scale image recognition by developing very deep convolutional networks, a foundational contribution evidenced by over 160,000 citations and near-universal independent adoption.
The researcher established a foundational theoretical framework for multiple view geometry in computer vision, codified in a seminal textbook that has become a standard reference in the field.
The researcher established the PASCAL VOC Challenge, a seminal benchmark that standardized visual object class recognition and catalyzed widespread adoption of rigorous evaluation in computer vision.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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