Shuyong Gao

998 citations
24 papers · 571 indexed · 1 hit paper · h-index 8
Topics
Emotion and Mood Recognition (9 papers)Visual Attention and Saliency Detection (7 papers)Advanced Image and Video Retrieval Techniques (7 papers)
Partner nations
ChinaItalySwitzerland

In The Last Decade

Shuyong Gao

21 papers receiving 563 citations

Hit Papers

A systematic review on affective computing: emotion model...2022202620232024202250100150200250

Peers

Shuyong Gao
Comparison fields: 5 of 73
  • Computer Vision and Pattern Recognition 287
  • Experimental and Cognitive Psychology 269
  • Artificial Intelligence 119
  • Cognitive Neuroscience 107
  • Social Psychology 59
Replace Mohan Karnati with:
Mohan Karnati India
Ciprian Corneanu Spain
Evangelos Sarıyanidi United States
Marc Oliu Simón Spain
Shreya Ghosh Australia
Yante Li Finland
Antoine Toisoul United Kingdom
Gianluca Donato United States
Adrià Recasens United States
S L Happy India
Shuyong Gao relative to Mohan Karnati India Mohan Karnati's profile →
Citations per field
00.5×1.5×
Mohan Karnati · 1×
Citations per year

Countries citing papers authored by Shuyong Gao

Since Specialization
Citations

This map shows the geographic impact of Shuyong Gao's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Shuyong Gao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shuyong Gao more than expected).

Fields of papers citing papers by Shuyong Gao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shuyong Gao. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Shuyong Gao. The network helps show where Shuyong Gao may publish in the future.

Co-authorship network of co-authors of Shuyong Gao

This figure shows the co-authorship network connecting the top 25 collaborators of Shuyong Gao. A scholar is included among the top collaborators of Shuyong Gao based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Shuyong Gao. Shuyong Gao is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 1
3 0
4 0
5 3
6 2
7 7
8 8
9 7
10 2
11 4
12 61
13 2
14 3
15 4
16 39
17 2
18 33
19
A systematic review on affective computing: emotion models, databases, and recent advancesbreakdown →
298
20 1

About Shuyong Gao

Shuyong Gao is a scholar working on Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology and Computer Graphics and Computer-Aided Design, having authored 24 papers that have together received 571 indexed citations. Recurring topics across this work include Emotion and Mood Recognition (9 papers), Visual Attention and Saliency Detection (7 papers) and Advanced Image and Video Retrieval Techniques (7 papers). The work is most often cited by research in Experimental and Cognitive Psychology (269 citations), Computer Vision and Pattern Recognition (287 citations) and Human-Computer Interaction (48 citations). Shuyong Gao has collaborated with scholars based in China, Italy and Switzerland. Frequent co-authors include Wenqiang Zhang, Yixuan Sun, Wei Zhang, Weifeng Ge, Yan Wang, Wei Song, Antonio Liotta, Dawei Yang, Xinlei Li and Wei Tao. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Information Sciences and IEEE Transactions on Industrial Informatics.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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