Saining Xie Google Scholar: h-index, Total Citations, and Citation Map
Saining Xie's h-index is 58 (90 i10-index, 111,241+ total citations across 102+ publications) according to Google Scholar as of October 2026. Saining Xie is affiliated with AMI Labs; New York University.
Saining Xie is a researcher affiliated with AMI Labs; New York University, specializing in artificial intelligence, computer vision, machine learning. Their work has been cited 111,241 times. This profile visualizes their global influence, highlighting strong citation networks in China.
Saining Xie's Citation Metrics
Bibliometric impact based on 102 indexed publications.
- H-Index
- 58
- i10-Index
- 90
- Total Citations
- 111,241
- Citing Countries
- 80
As of October 2026.
Saining Xie has an h-index of 58 and 111,241 total citations across 102 publications, with research cited by institutions in 80 countries.
Download Exports (PNG, CSV, Poster)
Free Viewing Saining Xie's citation map is always free. Pay once to download poster, PNG, and CSV files for offline use or your visa packet.
Showing partial map from open academic databases
Upgrade ($39.98) for the complete Google Scholar crawl.
Global Impact Map
Visualizing the geographic distribution of institutions that have cited your work.
Starting…
Pins will appear here as institutions are resolved — no need to refresh.
Momentum Contrast for Unsupervised Visual Representation Learning
202020,891
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.
1058 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher advanced 3D point cloud recognition by introducing attentional shape context methods and pioneering unsupervised contrastive pre-training frameworks for data-efficient scene understanding.
The researcher established a foundational framework for analyzing CLIP data, subsequently pioneering critical evaluations of visual capabilities and open-source architectures in multimodal large language models.
The researcher developed Momentum Contrast, a seminal framework for unsupervised visual representation learning that has achieved widespread independent adoption across the computer vision community.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
Related Guides
Learn how to use citation maps for your research and visa applications.
About Saining Xie's research
Saining Xie is a researcher in artificial intelligence, computer vision and machine learning at AMI Labs; New York University. Their work has been cited 111,241 times across 102 publications (h-index 58), according to Google Scholar.
Their most-cited work, “Momentum Contrast for Unsupervised Visual Representation Learning” (2020), has accumulated 20,891 citations. Other influential works include “Aggregated Residual Transformations for Deep Neural Networks” (2017) with 17,044 citations and “Masked Autoencoders are Scalable Vision Learners” (2022) with 16,310 citations.
Citations of Saining Xie'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.
