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Researcher · Nanjing University · China

Wu-Jun Li

Wu-Jun Li publishes mostly in Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics, on topics such as Advanced Image and Video Retrieval Techniques, Stochastic Gradient Optimization Techniques and Sparse and Compressive Sensing Techniques.

ORCID 0000-0003-2499-4006 OpenAlex
World rank
#500,299
of 7,710,434 ranked researchers
Rank in China
#61,667
of 978,568
Works
143
Citations
4.7k
Citations per work
32.9
h-index
28
i10-index 48Top 6.1% in Computer Vision and Pattern Recognition
Wu-Jun Li

What does Wu-Jun Li research?

Wu-Jun Li's work is mostly in Computer Science (51%), Engineering (8%), Medicine (6%).

Physical Sciences: 84 works · 58.9% of their workComputer Science: 73 works · 51.1% of their workComputer Vision and Pattern Recognition: 38 works · 26.9% of their workArtificial Intelligence: 35 works · 24.2% of their workEngineering: 11 works · 7.8% of their workComputational Mechanics: 6 works · 4.4% of their workElectrical and Electronic Engineering: 5 works · 3.4% of their workEverything else: 50 works · 35.0% of their workHealth Sciences: 9 works · 6.1% of their workMedicine: 9 works · 6.1% of their workSurgery: 5 works · 3.2% of their workOncology: 4 works · 2.9% of their workTHEIR WORKWu-Jun Li143works
Physical Sciences59%Health Sciences6%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
    1. Computer Science7351.1%
    2. Engineering117.8%
  2. Everything else5035.0%
    1. Medicine96.1%
  1. 1Computer Vision and Pattern Recognition27% of their works
  2. 2Artificial Intelligence24% of their works
  3. 3Computational Mechanics4% of their works
  4. 4Electrical and Electronic Engineering3% of their works
  5. 5Surgery3% of their works
  6. 6Oncology3% of their works

Research topics

Which keywords describe Wu-Jun Li's research?

The keywords of their largest research topics: Deep Learning, Convex Optimization, Sparse Representation, Feature Matching, Local Descriptors, Local Binary Patterns, Random Projections and Stochastic Gradient Descent.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most. The words come from the keyword list OpenAlex gives each topic, not from the text of the papers, so a word can appear that none of the papers uses.

All 21 words, with their numbers
  1. Deep Learning653 topics
  2. Convex Optimization412 topics
  3. Sparse Representation322 topics
  4. Feature Matching271 topic
  5. Local Descriptors271 topic
  6. Local Binary Patterns252 topics
  7. Random Projections231 topic
  8. Stochastic Gradient Descent231 topic
  9. Machine Learning222 topics
  10. Compressed Sensing181 topic
  11. Signal Recovery181 topic
  12. Object Tracking151 topic
  13. Visual Tracking151 topic
  14. Dimensionality Reduction141 topic
  15. Face Recognition141 topic
  16. Content-Based Retrieval121 topic
  17. Video Summarization121 topic
  18. Content-Based Image Retrieval111 topic
  19. Differential Privacy111 topic
  20. Federated Learning111 topic
  21. Shape Matching111 topic

Where does Wu-Jun Li work?

Wu-Jun Li's main affiliation in the publication record is Nanjing University, China.

How many publications and citations does Wu-Jun Li have?

OpenAlex counts 143 works and 4,710 citations for Wu-Jun Li, an h-index of 28, ranking #500,299 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised citation excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Other researchers named Wu-Jun Li

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

Discussion

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