Errui Ding

11.1k citations
114 papers · 4.9k indexed · 4 hit papers · h-index 37

Errui Ding

105 papers receiving 4.7k citations

Hit Papers

Group DETR: Fast DETR Training with ...1072019202620212023100200300

Peers

Errui Ding
Comparison fields: 5 of 132
  • Computer Vision and Pattern Recognition 4.3k
  • Media Technology 682
  • Computer Graphics and Computer-Aided Design 225
  • Artificial Intelligence 1.2k
  • Human-Computer Interaction 145
Replace Lizhuang Ma with:
Lizhuang Ma China
Junhui Hou Hong Kong
Shengfeng He China
Shenghua Gao China
Sven Dickinson Canada
Guanbin Li China
Song–Hai Zhang China
Kaiqi Huang China
Changick Kim South Korea
Oncel Tuzel United States
Errui Ding relative to Lizhuang Ma China Lizhuang Ma's profile →
Citations per field
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Lizhuang Ma · 1×
Citations per year

Countries citing papers authored by Errui Ding

Since Specialization
Citations

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

Fields of papers citing papers by Errui Ding

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20252
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11 202320
12 20232
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14 202228
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16 202151
17 2019213
18
Multi-camera vehicle tracking and re-identification based on visual and spatial-temporal features
201923
19 2019131
20
Compact Generalized Non-local Network
201833

About Errui Ding

Errui Ding is a scholar working on Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design, Health Informatics, Media Technology and Artificial Intelligence, having authored 114 papers that have together received 4.9k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (32 papers), Advanced Vision and Imaging (20 papers), Generative Adversarial Networks and Image Synthesis (19 papers), Video Surveillance and Tracking Methods (19 papers), Human Pose and Action Recognition (17 papers), Advanced Image and Video Retrieval Techniques (16 papers), Domain Adaptation and Few-Shot Learning (13 papers) and Face recognition and analysis (12 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (4.3k citations), Media Technology (682 citations), Computer Graphics and Computer-Aided Design (225 citations), Artificial Intelligence (1.2k citations) and Human-Computer Interaction (145 citations). Errui Ding has collaborated with scholars based in China, Australia and Singapore. Frequent co-authors include Junyu Han, Shilei Wen, Mengyang Feng, Huchuan Lu, Tianwei Lin, Jingtuo Liu, Jingdong Wang, Xiao Liu, Xiao Tan and Dongliang He. Their work appears in journals such as International Journal of Computer Vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Intelligent Transportation Systems, IEEE Robotics and Automation Letters and IEEE Transactions on Circuits and Systems for Video Technology.

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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