Dahua Lin: h-index, Total Citations, and Citation Map
Dahua Lin's h-index is 131 (388 i10-index, 94,693+ total citations across 561+ publications) according to Google Scholar as of June 2026. Dahua Lin is affiliated with The Chinese University of Hong Kong.
Dahua Lin is a researcher affiliated with The Chinese University of Hong Kong, specializing in computer vision, machine learning, probabilistic inference. Their work has been cited 94,693 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Dahua Lin's Citation Metrics
Bibliometric impact based on 561 indexed publications.
- H-Index
- 131
- i10-Index
- 388
- Total Citations
- 94,693
- Citing Countries
- 48
As of June 2026.
Dahua Lin has an h-index of 131 and 94,693 total citations across 561 publications, with research cited by institutions in 48 countries.
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We've mapped 5,000 of 94,693 citations for Dahua Lin
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Spatial temporal graph convolutional networks for skeleton-based action recognition
20187,122
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.
4 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered spatial-temporal graph convolutional networks for skeleton-based action recognition, establishing a foundational framework that subsequent independent studies have widely adopted and refined.
The researcher established foundational practices for deep action recognition using temporal segment networks, a seminal contribution widely adopted by the independent computer vision community.
The researcher developed a seminal unsupervised feature learning method using non-parametric instance discrimination, establishing a foundational approach for representation learning without labeled data.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Dahua Lin's research
Dahua Lin is a researcher in computer vision, machine learning and probabilistic inference at The Chinese University of Hong Kong. Their work has been cited 94,693 times across 561 publications (h-index 131), according to Google Scholar.
Their most-cited work, “Spatial temporal graph convolutional networks for skeleton-based action recognition” (2018), has accumulated 7,122 citations. Other influential works include “Temporal segment networks: Towards good practices for deep action recognition” (2016) with 5,523 citations and “Unsupervised feature learning via non-parametric instance discrimination” (2018) with 5,395 citations.
Citations of Dahua Lin'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.
