Lida LI: h-index, Total Citations, and Citation Map
Lida LI's h-index is 9 (9 i10-index, 3,795+ total citations across 15+ publications) according to Google Scholar as of May 2026. Lida LI is affiliated with PhD, The Hong Kong Polytechnic University.
Lida LI is a researcher affiliated with PhD, The Hong Kong Polytechnic University, specializing in computer vision, machine learning, pattern recognition. Their work has been cited 3,795 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Lida LI's Citation Metrics
Bibliometric impact based on 15 indexed publications.
- H-Index
- 9
- i10-Index
- 9
- Total Citations
- 3,795
- Citing Countries
- 49
As of May 2026.
Lida LI has an h-index of 9 and 3,795 total citations across 15 publications, with research cited by institutions in 49 countries.
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Global Impact Map
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Top Cited Works
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Simam: A simple, parameter-free attention module for convolutional neural networks
20212,689
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.
670 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed block-wise statistical features and LC-KSVD for 3D ear identification, extending the framework to collaborative representation for palmprint recognition.
The researcher developed a grid anchor-based approach for reliable and efficient image cropping, establishing a foundational method and benchmark that has been widely adopted by the independent computer vision community.
The researcher developed Simam, a parameter-free attention module for convolutional neural networks, establishing a foundational, highly cited approach to efficient feature enhancement in deep learning.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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