Kai Wu: h-index, Total Citations, and Citation Map
Kai Wu's h-index is 12 (15 i10-index, 883+ total citations across 27+ publications) according to Google Scholar as of May 2026. Kai Wu is affiliated with ByteDance.
Kai Wu is a researcher affiliated with ByteDance, specializing in MLLM: wukaiwork[At]gmail.com. Their work has been cited 883 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Kai Wu's Citation Metrics
Bibliometric impact based on 27 indexed publications. Of these, 18 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 12
- i10-Index
- 15
- Total Citations
- 883
- Citing Countries
- 35
As of May 2026.
Kai Wu has an h-index of 12 and 883 total citations across 27 publications, with research cited by institutions in 35 countries.
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Global Impact Map
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Top Cited Works
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Class-aware contrastive semi-supervised learning
2022187
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.
376 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher pioneered class-aware contrastive semi-supervised learning, establishing a foundational framework that subsequent work extended to unsupervised continual anomaly detection via contrastively-learned prompts.
The researcher advanced robust visual-centric 3D object detection through the BEVHeight++ framework, establishing a significant methodological contribution evidenced by substantial independent scholarly adoption.
The researcher developed a hierarchical semantic segmentation and dynamic homography estimation framework for automated road marking damage inspection.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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