Tuong Do

516 citations
16 papers · 202 · h-index 6

Impact in

    • Multimodal Machine Learning Applications
    • Advanced Image and Video Retrieval Techniques
    • Human Pose and Action Recognition
    • Advanced Neural Network Applications
    • Medical Image Segmentation Techniques
    • Privacy-Preserving Technologies in Data
    • Domain Adaptation and Few-Shot Learning

Papers in

Tuong Do

13 papers receiving 198 citations

Peers

Tuong Do
Comparison fields: 5 of 57
  • Computer Vision and Pattern Recognition 105
  • Artificial Intelligence 125
  • Computational Mathematics 2
  • Health Informatics 3
  • Neurology 12
Replace Erman Tjiputra with:
Erman Tjiputra United Kingdom
Congbo Ma Australia
Ayush K Tarun India
Andrea Cossu Italy
B. Uma Maheswari India
Marcin Gabryel Poland
Mohammad Babaeizadeh United States
J. Jaya India
Raghubir Singh United Kingdom
Abdulaziz M. Alayba Saudi Arabia
Tuong Do relative to Erman Tjiputra United Kingdom Erman Tjiputra's profile →
Citations per field
00.5×1.5×
Erman Tjiputra · 1×
Citations per year

Countries citing papers authored by Tuong Do

Since Specialization
Citations

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

Fields of papers citing papers by Tuong Do

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 21 scholars most cited alongside Tuong Do, 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 Tuong Do Line = papers co-authored together Tuong Do links everyone, so they are left out of the graph.

All Works

16 of 16 papers shown
#Work
1 202275
2 201938
3 202234
4 202217
5 202311
6 20196
7 20215
8 20194
9 20234
10 20233
11 20243
12 20241
13 20181
14 20240
15 20250
16 20250

About Tuong Do

Tuong Do is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Signal Processing and Automotive Engineering, having authored 16 papers that have together received 202 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (4 papers), Privacy-Preserving Technologies in Data (3 papers), Human Motion and Animation (3 papers), Domain Adaptation and Few-Shot Learning (3 papers), Multimodal Machine Learning Applications (2 papers), Human Pose and Action Recognition (2 papers), Music and Audio Processing (2 papers) and Vehicular Ad Hoc Networks (VANETs) (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (105 citations), Artificial Intelligence (125 citations), Computational Mathematics (2 citations), Health Informatics (3 citations) and Neurology (12 citations). Tuong Do has collaborated with scholars based in United Kingdom, Vietnam and Taiwan. Frequent co-authors include Quang D. Tran, Erman Tjiputra, Anh Nguyen, Huy Dat Tran, Thanh-Toan Do, Huy Q. Tran, Hoang Chinh Nguyen, Huy Tran, Vuong T. Pham and Anh‐Tu Nguyen. Their work appears in journals such as IEEE Transactions on Medical Imaging, ACM Transactions on Graphics, Computational Intelligence and Neuroscience, SHILAP Revista de lepidopterología and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

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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