Dongliang Chang
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 2%
- Electrical and Electronic Engineering
- Media Technology top 5%
- Renewable Energy, Sustainability and the Environment top 10%
- Topics
- Advanced Neural Network Applications (24 papers)Domain Adaptation and Few-Shot Learning (22 papers)Advanced Image and Video Retrieval Techniques (17 papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine IntelligenceApplied EnergyIEEE Transactions on Image Processing
- Partner nations
- ChinaUnited KingdomDenmark
In The Last Decade
Dongliang Chang
37 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 103
- Computer Vision and Pattern Recognition 641
- Artificial Intelligence 588
- Electrical and Electronic Engineering 228
- Media Technology 132
- Renewable Energy, Sustainability and the Environment 121
Countries citing papers authored by Dongliang Chang
This map shows the geographic impact of Dongliang Chang'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 Dongliang Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dongliang Chang more than expected).
Fields of papers citing papers by Dongliang Chang
This network shows the impact of papers produced by Dongliang Chang. 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 Dongliang Chang. The network helps show where Dongliang Chang may publish in the future.
Co-authorship network of co-authors of Dongliang Chang
This figure shows the co-authorship network connecting the top 25 collaborators of Dongliang Chang. A scholar is included among the top collaborators of Dongliang Chang 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 Dongliang Chang. Dongliang Chang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 4 | |
| 5 | 1 | |
| 6 | 9 | |
| 7 | 3 | |
| 8 | 37 | |
| 9 | 8 | |
| 10 | 81 | |
| 11 | 32 | |
| 12 | 30 | |
| 13 | 3 | |
| 14 | 8 | |
| 15 | 3 | |
| 16 | 118 | |
| 17 | 105 | |
| 18 | 230 | |
| 19 | 1 | |
| 20 | 29 |
About Dongliang Chang
Dongliang Chang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 41 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (24 papers), Domain Adaptation and Few-Shot Learning (22 papers) and Advanced Image and Video Retrieval Techniques (17 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (641 citations), Artificial Intelligence (588 citations) and Media Technology (132 citations). Dongliang Chang has collaborated with scholars based in China, United Kingdom and Denmark. Frequent co-authors include Zhanyu Ma, Jiyang Xie, Jun Guo, Xiaoxu Li, Yi-Zhe Song, Yifeng Ding, Ayan Kumar Bhunia, Ming Wu, Qie Sun and Luyao Liu. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Applied Energy and IEEE Transactions on Image Processing.
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.