Songlin Dong

862 total citations · 1 hit paper
19 papers, 482 citations indexed

About

Songlin Dong is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Songlin Dong has authored 19 papers receiving a total of 482 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Artificial Intelligence, 13 papers in Computer Vision and Pattern Recognition and 3 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Songlin Dong's work include Domain Adaptation and Few-Shot Learning (15 papers), Multimodal Machine Learning Applications (10 papers) and Machine Learning and ELM (7 papers). Songlin Dong is often cited by papers focused on Domain Adaptation and Few-Shot Learning (15 papers), Multimodal Machine Learning Applications (10 papers) and Machine Learning and ELM (7 papers). Songlin Dong collaborates with scholars based in China, Singapore and Poland. Songlin Dong's co-authors include Yihong Gong, Xiaopeng Hong, Xiaoyu Tao, Xing Wei, Xinyuan Chang, Jingang Shi, Yu Liu, Xinyuan Gao, Shaokun Wang and Yuhang He and has published in prestigious journals such as IEEE Transactions on Medical Imaging, Pattern Recognition and IEEE Transactions on Neural Networks and Learning Systems.

In The Last Decade

Songlin Dong

16 papers receiving 475 citations

Hit Papers

Few-Shot Class-Incremental Learning 2020 2026 2022 2024 2020 50 100 150 200 250

Peers

Songlin Dong
Comparison fields: 5 of 56
  • Artificial Intelligence 392
  • Computer Vision and Pattern Recognition 232
  • Radiology, Nuclear Medicine and Imaging 59
  • Media Technology 23
  • Cancer Research 22
Replace Xinyuan Chang with:
Xinyuan Chang China
Christian Simon Australia
Rongyao Fang Hong Kong
Qianfen Jiao China
Teli Ma China
Nan Song China
Yukang Ding China
Xinyuan Chang China View profile →
Citations per field, relative to Songlin Dong
Songlin Dong · 1×
Citations per year, relative to Songlin Dong
Songlin Dong · 1×

Countries citing papers authored by Songlin Dong

Since Specialization
Citations

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

Fields of papers citing papers by Songlin Dong

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Songlin Dong

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

All Works

19 of 19 papers shown
# Work Indexed citations
1 0
2 0
3 8
4 1
5 0
6 2
7 8
8 2
9 24
10 5
11 2
12 8
13 4
14 13
15 37
16 11
17 2
18 96
19
Few-Shot Class-Incremental Learning breakdown →
259

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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