Dezhi Han

517 citations
14 papers · 366 indexed · 1 hit paper · h-index 7
Topics
Multimodal Machine Learning Applications (11 papers)Advanced Image and Video Retrieval Techniques (9 papers)Domain Adaptation and Few-Shot Learning (8 papers)
Partner nations
ChinaTaiwanAustralia

In The Last Decade

Dezhi Han

11 papers receiving 360 citations

Hit Papers

MPCCT: Multimodal vision-language learning paradigm with ...20232026202420252023255075

Peers

Dezhi Han
Comparison fields: 5 of 80
  • Computer Vision and Pattern Recognition 165
  • Artificial Intelligence 135
  • Biomedical Engineering 32
  • Materials Chemistry 27
  • Electrical and Electronic Engineering 26
Replace Qinwei Fan with:
Qinwei Fan China
Ahmed G. Radwan Egypt
Mingxia Li China
Tianwei Xu China
Changjun Zhou China
Omar Kahouli Tunisia
Dezhi Han relative to Qinwei Fan China Qinwei Fan's profile →
Citations per field
00.5×1.5×2.1×
Qinwei Fan · 1×
Citations per year

Countries citing papers authored by Dezhi Han

Since Specialization
Citations

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

Fields of papers citing papers by Dezhi Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dezhi Han

This figure shows the co-authorship network connecting the top 25 collaborators of Dezhi Han. A scholar is included among the top collaborators of Dezhi Han 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 Dezhi Han. Dezhi Han is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

14 of 14 papers shown
#WorkIndexed citations
1 5
2 0
3 0
4 2
5 1
6 39
7 0
8 33
9 4
10 9
11 84
12
MPCCT: Multimodal vision-language learning paradigm with context-based compact Transformerbreakdown →
96
13 53
14 40

About Dezhi Han

Dezhi Han is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Environmental Engineering, having authored 14 papers that have together received 366 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (11 papers), Advanced Image and Video Retrieval Techniques (9 papers) and Domain Adaptation and Few-Shot Learning (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (165 citations), Artificial Intelligence (135 citations) and Modeling and Simulation (16 citations). Dezhi Han has collaborated with scholars based in China, Taiwan and Australia. Frequent co-authors include Chongqing Chen, Chin‐Chen Chang, Xiang Shen, Jun Wang, Huafeng Wu, Kuan‐Ching Li, Muhammad Khurram Khan, Jiahao Zhao, Zihan Guo and Ammar Oad. Their work appears in journals such as Expert Systems with Applications, IEEE Access and Pattern Recognition.

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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