Science Explorer Back to mapMap
Discussion
Researcher · Queen Mary University of London · United Kingdom

Shaogang Gong

Shaogang Gong publishes mostly in Computer Vision and Pattern Recognition, Artificial Intelligence and Biomedical Engineering, on topics such as Video Surveillance and Tracking Methods, Human Pose and Action Recognition and Face recognition and analysis.

ORCID 0000-0001-8156-2299 OpenAlex
World rank
#7,903
of 7,710,434 ranked researchers
Rank in United Kingdom
#511
of 367,258
Works
480
Citations
37k
Citations per work
76.2
h-index
97
i10-index 300Top 0.1% in Computer Vision and Pattern Recognition
Shaogang Gong

What does Shaogang Gong research?

Shaogang Gong's work is mostly in Computer Science (82%), Engineering (6%).

Physical Sciences: 425 works · 88.6% of their workComputer Science: 395 works · 82.3% of their workComputer Vision and Pattern Recognition: 297 works · 61.8% of their workArtificial Intelligence: 79 works · 16.5% of their workSignal Processing: 13 works · 2.8% of their workHuman-Computer Interaction: 6 works · 1.2% of their workEngineering: 30 works · 6.3% of their workBiomedical Engineering: 25 works · 5.2% of their workMedia Technology: 5 works · 1.1% of their workEverything else: 55 works · 11.4% of their workTHEIR WORKShaogang Gong480works
Physical Sciences89%

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 Science39582.3%
    2. Engineering306.3%
  2. Everything else5511.4%
  1. 1Computer Vision and Pattern Recognition62% of their works
  2. 2Artificial Intelligence16% of their works
  3. 3Biomedical Engineering5% of their works
  4. 4Signal Processing3% of their works
  5. 5Human-Computer Interaction1% of their works
  6. 6Media Technology1% of their works

Research topics

Which keywords describe Shaogang Gong's research?

The keywords of their largest research topics: Deep Learning, Convolutional Neural Networks, Pose Estimation, Object Tracking, Visual Tracking, Action Recognition, Neural Networks and Spatiotemporal Features.

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 20 words, with their numbers
  1. Deep Learning7197 topics
  2. Convolutional Neural Networks2812 topics
  3. Pose Estimation2312 topics
  4. Object Tracking1931 topic
  5. Visual Tracking1931 topic
  6. Action Recognition1431 topic
  7. Neural Networks1432 topics
  8. Spatiotemporal Features1431 topic
  9. Face Recognition881 topic
  10. Facial Landmark Detection881 topic
  11. Feature Matching841 topic
  12. Local Descriptors841 topic
  13. Domain Adaptation771 topic
  14. Transfer Learning771 topic
  15. Anomaly Detection741 topic
  16. Unsupervised741 topic
  17. Image Captioning691 topic
  18. Visual Question Answering691 topic
  19. Gait Recognition681 topic
  20. Human Identification681 topic

Where does Shaogang Gong work?

Shaogang Gong's main affiliation in the publication record is Queen Mary University of London, United Kingdom.

How many publications and citations does Shaogang Gong have?

OpenAlex counts 480 works and 36,590 citations for Shaogang Gong, an h-index of 97, ranking #7,903 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.

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

Ask a question or share a note about Shaogang Gong. Keep it to the research: no personal attacks, advertising or links. Any reader can report a post, which collapses it for everyone until it is opened.