Philip Torr Google Scholar: h-index, Total Citations, and Citation Map
Philip Torr's h-index is 155 (601 i10-index, 139,181+ total citations across 1,000+ publications) according to Google Scholar as of October 2026. Philip Torr is affiliated with University of Oxford.
Philip Torr is a researcher affiliated with University of Oxford, specializing in Department of Engineering. Their work has been cited 139,181 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Philip Torr's Citation Metrics
Bibliometric impact based on 1,000 indexed publications.
- H-Index
- 155
- i10-Index
- 601
- Total Citations
- 139,181
- Citing Countries
- 54
As of October 2026.
Philip Torr has an h-index of 155 and 139,181 total citations across 1000 publications, with research cited by institutions in 54 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Philip Torr's citation map is always free. Pay once to download poster, PNG, and CSV files for offline use or your visa packet.
Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
Starting…
Pins will appear here as institutions are resolved — no need to refresh.
Learning to compare: Relation network for few-shot learning
20186,578
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.
32 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher introduced a relation network framework for few-shot learning, establishing a seminal approach to comparing samples that has become a foundational reference in computer vision.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
Related Guides
Learn how to use citation maps for your research and visa applications.
About Philip Torr's research
Philip Torr is a researcher in Department of Engineering at University of Oxford. Their work has been cited 139,181 times across 1,000 publications (h-index 155), according to Google Scholar.
Their most-cited work, “Learning to compare: Relation network for few-shot learning” (2018), has accumulated 6,578 citations. Other influential works include “Fully-convolutional siamese networks for object tracking” (2016) with 6,244 citations and “Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers” (2021) with 5,280 citations.
Citations of Philip Torr's research come primarily from China, United States and United Kingdom, reflecting international research impact across 5+ countries. The interactive citation map above shows the full geographic distribution of the institutions citing this work.
