Shaozi Li

14.8k citations
224 papers · 8.5k indexed · 6 hit papers · h-index 37

Shaozi Li

205 papers receiving 8.3k citations

Hit Papers

Mutual learning w...57201720262020202350010001.5k

Peers

Shaozi Li
Comparison fields: 5 of 169
  • Computer Vision and Pattern Recognition 6.1k
  • Artificial Intelligence 2.6k
  • Media Technology 440
  • Human-Computer Interaction 220
  • Biomedical Engineering 1.6k
Replace Gao Huang with:
Gao Huang China
Bingbing Ni China
Xiatian Zhu United Kingdom
Zhuang Liu China
Nianyin Zeng China
Timothy M. Hospedales United Kingdom
Mingkui Tan China
Haoqi Fan United States
Sergey Karayev United States
Chang Xu China
Shaozi Li relative to Gao Huang China Gao Huang's profile →
Citations per field
00.5×1.5×2.1×
Gao Huang · 1×
Citations per year

Countries citing papers authored by Shaozi Li

Since Specialization
Citations

This map shows the geographic impact of Shaozi Li'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 Shaozi Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shaozi Li more than expected).

Fields of papers citing papers by Shaozi Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shaozi Li. 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 Shaozi Li. The network helps show where Shaozi Li may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Shaozi Li, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Shaozi Li Line = papers co-authored together Shaozi Li links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20253
2 20243
3 20242
4 20244
5 202423
6 20238
7 20231
8 20230
9 20232
10 202229
11 202274
12 20229
13 20226
14 202011
15 20208
16 201930
17 20182
18
Re-ranking Person Re-identification with k-Reciprocal Encodingbreakdown →
20171067
19 20135
20 201014

About Shaozi Li

Shaozi Li is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Human-Computer Interaction, having authored 224 papers that have together received 8.5k indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (50 papers), Human Pose and Action Recognition (35 papers), Advanced Image and Video Retrieval Techniques (35 papers), Advanced Neural Network Applications (32 papers), Image Retrieval and Classification Techniques (31 papers), Text and Document Classification Technologies (29 papers), Face and Expression Recognition (19 papers) and Gait Recognition and Analysis (18 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (6.1k citations), Artificial Intelligence (2.6k citations) and Media Technology (440 citations). Shaozi Li has collaborated with scholars based in China, Italy and Canada. Frequent co-authors include Zhun Zhong, Liang Zheng, Zhiming Luo, Yi Yang, Donglin Cao, Guoliang Kang, Sheng Lian, Jia Zhang, Xiao Xiao and Zhedong Zheng. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Expert Systems with Applications.

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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