Ming-Ming Cheng Google Scholar: h-index, Total Citations, and Citation Map
Ming-Ming Cheng's h-index is 116 (239 i10-index, 83,305+ total citations across 316+ publications) according to Google Scholar as of October 2026. Ming-Ming Cheng is affiliated with Nankai University.
Ming-Ming Cheng is a researcher affiliated with Nankai University, specializing in various fields. Their work has been cited 83,305 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Ming-Ming Cheng's Citation Metrics
Bibliometric impact based on 316 indexed publications. Of these, 18 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 116
- i10-Index
- 239
- Total Citations
- 83,305
- Citing Countries
- 72
As of October 2026.
Ming-Ming Cheng has an h-index of 116 and 83,305 total citations across 316 publications, with research cited by institutions in 72 countries.
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Global Impact Map
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Global contrast based salient region detection
20155,309
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 established a foundational framework for global contrast-based salient region detection, subsequently expanding the field through comprehensive surveys and specialized concealed object detection methods.
The researcher introduced a novel structure-measure metric for evaluating foreground maps, establishing a foundational standard that enabled subsequent advances in camouflaged object detection.
The researcher introduced Res2Net, a novel multi-scale backbone architecture that significantly advanced deep learning feature representation capabilities, as evidenced by its widespread adoption and high citation impact.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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About Ming-Ming Cheng's research
Ming-Ming Cheng is a researcher at Nankai University. Their work has been cited 83,305 times across 316 publications (h-index 116), according to Google Scholar.
Their most-cited work, “Global contrast based salient region detection” (2015), has accumulated 5,309 citations. Other influential works include “Res2Net: A New Multi-scale Backbone Architecture” (2021) with 4,284 citations and “Struck: Structured Output Tracking with Kernels” (2016) with 3,414 citations.
Citations of Ming-Ming Cheng'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.
