Jiaqi Wang: h-index, Total Citations, and Citation Map
Jiaqi Wang's h-index is 57 (97 i10-index, 27,009+ total citations across 137+ publications) according to Google Scholar as of June 2026. Jiaqi Wang is affiliated with JD.Com | Shanghai Innovation Institute (SII).
Jiaqi Wang is a researcher affiliated with JD.Com | Shanghai Innovation Institute (SII), specializing in Computer Vision, Multi-modal Learning, VLM. Their work has been cited 27,009 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Jiaqi Wang's Citation Metrics
Bibliometric impact based on 137 indexed publications.
- H-Index
- 57
- i10-Index
- 97
- Total Citations
- 27,009
- Citing Countries
- 38
As of June 2026.
Jiaqi Wang has an h-index of 57 and 27,009 total citations across 137 publications, with research cited by institutions in 38 countries.
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We've mapped 5,000 of 27,009 citations for Jiaqi Wang
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Global Impact Map
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MMDetection: Open mmlab detection toolbox and benchmark
20194,589
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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.
14 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher established a foundational open-source object detection framework and advanced end-to-end detection and long-tailed segmentation methods, driving widespread independent adoption in computer vision.
The researcher established a comprehensive benchmark for evaluating the all-around capabilities of multi-modal models, providing a critical standard for assessing holistic performance in the field.
The researcher developed MMDetection, an open-source object detection toolbox and benchmark that has become a widely adopted standard in the computer vision community.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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