De Cheng

1.8k citations
24 papers · 1.3k · 1 hit paper · h-index 13

Impact in

Papers in

De Cheng

24 papers receiving 1.3k citations

De Cheng's Hit Papers

Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function 2016 · 896 citations
8960+3+6Years since publication250500750

Peers

De Cheng
Comparison fields: 5 of 92
  • Computer Vision and Pattern Recognition 1.1k
  • Biomedical Engineering 456
  • Artificial Intelligence 200
  • Media Technology 50
  • Safety, Risk, Reliability and Quality 51
Replace Yang Wu with:
Yang Wu China
Youzhi Gu China
Hehe Fan China
Zhilan Hu China
Peixi Peng China
Hong-Xing Yu China
Haiyu Zhao Singapore
Shuyang Sun United Kingdom
De Cheng relative to Yang Wu China Yang Wu's profile →
Citations per field
00.5×8.4×
Yang Wu · 1×
Citations per year

Countries citing papers authored by De Cheng

Since Specialization
Citations

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

Fields of papers citing papers by De Cheng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside De Cheng, 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 De Cheng Line = papers co-authored together De Cheng links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Person Re-identification by Multi-Channel Parts-Based CNN with Improved Triplet Loss Function
Hit paper breakdown →
2016896
2 201860
3 201756
4 201846
5 201845
6 201741
7 201822
8 201721
9 201917
10 201815
11 201713
12 201713
13 201712
14 200911
15 201411
16 20189
17 20169
18 20188
19 20164
20 20083

About De Cheng

De Cheng is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Biomedical Engineering, Hepatology and Infectious Diseases, having authored 24 papers that have together received 1.3k indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (11 papers), Human Pose and Action Recognition (7 papers), Advanced Image and Video Retrieval Techniques (6 papers), Face recognition and analysis (5 papers), Advanced Neural Network Applications (5 papers), Gait Recognition and Analysis (4 papers), Hepatitis C virus research (3 papers) and Face and Expression Recognition (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.1k citations), Biomedical Engineering (456 citations), Artificial Intelligence (200 citations), Media Technology (50 citations) and Safety, Risk, Reliability and Quality (51 citations). De Cheng has collaborated with scholars based in China, United States and Australia. Frequent co-authors include Yihong Gong, Nanning Zheng, Jinjun Wang, Sanping Zhou, Weiwei Shi, Huaxiang Zhang, Xiaoyu Tao, Xiaojun Chang, Yi Yang and Zhihui Li. Their work appears in journals such as Neurocomputing, Pattern Recognition, Multimedia Tools and Applications, European Journal of Clinical Microbiology & Infectious Diseases and Pattern Recognition Letters.

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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