Siliang Tang

2.8k total citations
122 papers, 1.6k citations indexed

About

Siliang Tang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Siliang Tang has authored 122 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Artificial Intelligence, 67 papers in Computer Vision and Pattern Recognition and 9 papers in Information Systems. Recurrent topics in Siliang Tang's work include Multimodal Machine Learning Applications (40 papers), Topic Modeling (34 papers) and Natural Language Processing Techniques (26 papers). Siliang Tang is often cited by papers focused on Multimodal Machine Learning Applications (40 papers), Topic Modeling (34 papers) and Natural Language Processing Techniques (26 papers). Siliang Tang collaborates with scholars based in China, United States and Singapore. Siliang Tang's co-authors include Yueting Zhuang, Fei Wu, Jun Xiao, Yi Yang, Yanpeng Cao, Yanlong Cao, Yu Zhou, Zewei He, Wenqiao Zhang and Yin Zhang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.

In The Last Decade

Siliang Tang

109 papers receiving 1.5k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Siliang Tang China 22 934 705 143 108 54 122 1.6k
Handong Zhao United States 20 1.3k 1.4× 1.0k 1.4× 169 1.2× 130 1.2× 75 1.4× 76 1.7k
Peiguang Jing China 16 706 0.8× 296 0.4× 109 0.8× 101 0.9× 65 1.2× 92 1.1k
Weili Guan China 23 925 1.0× 664 0.9× 62 0.4× 182 1.7× 62 1.1× 88 1.5k
Donglin Cao China 15 1.4k 1.5× 613 0.9× 73 0.5× 157 1.5× 77 1.4× 60 1.9k
Alexis Battle United States 5 481 0.5× 688 1.0× 94 0.7× 71 0.7× 109 2.0× 7 1.1k
Yujiu Yang China 20 1.0k 1.1× 558 0.8× 220 1.5× 73 0.7× 110 2.0× 128 1.7k
Hong-Han Shuai Taiwan 21 745 0.8× 494 0.7× 53 0.4× 138 1.3× 125 2.3× 119 1.5k
Mohammad Rahmati Iran 20 667 0.7× 528 0.7× 134 0.9× 104 1.0× 131 2.4× 104 1.5k

Countries citing papers authored by Siliang Tang

Since Specialization
Citations

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

Fields of papers citing papers by Siliang Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Siliang Tang

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

All Works

20 of 20 papers shown
1.
Gao, Fei, Shuo Zhang, Yueting Zhuang, et al.. (2025). Improving large models with small models: Lower costs and better performance. Neural Networks. 195. 108276–108276.
2.
Liu, Yongchao, et al.. (2025). GraphCLIP: Enhancing Transferability in Graph Foundation Models for Text-Attributed Graphs. 2183–2197. 3 indexed citations
3.
Liu, Yongchao, et al.. (2025). Graph Retrieval-Augmented Generation: A Survey. ACM Transactions on Information Systems. 44(2). 1–52. 3 indexed citations
4.
Tang, Siliang, et al.. (2025). EvidenceMap: Learning evidence analysis to unleash the power of small language models for biomedical question answering. Artificial Intelligence in Medicine. 169. 103246–103246.
5.
Zhuang, Yueting, et al.. (2024). Data Shunt: Collaboration of Small and Large Models for Lower Costs and Better Performance. Proceedings of the AAAI Conference on Artificial Intelligence. 38(10). 11249–11257. 3 indexed citations
6.
7.
Shen, Kai, Lingfei Wu, Siliang Tang, et al.. (2024). Ask Questions With Double Hints: Visual Question Generation With Answer-Awareness and Region-Reference. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 9648–9660.
8.
Wang, Guoming, et al.. (2024). WorldGPT: Empowering LLM as Multimodal World Model. 7346–7355. 13 indexed citations
9.
Wang, Xiaoqiang, Bang Liu, Siliang Tang, & Lingfei Wu. (2023). SkillQG: Learning to Generate Question for Reading Comprehension Assessment. 13833–13850. 1 indexed citations
10.
Wei, Longhui, et al.. (2023). Degeneration-Tuning: Using Scrambled Grid shield Unwanted Concepts from Stable Diffusion. 8900–8909. 8 indexed citations
11.
Tang, Siliang, et al.. (2023). RustGraph: Robust Anomaly Detection in Dynamic Graphs by Jointly Learning Structural-Temporal Dependency. IEEE Transactions on Knowledge and Data Engineering. 36(7). 3472–3485. 3 indexed citations
12.
Chen, Xiangnan, et al.. (2023). Global Structure Knowledge-Guided Relation Extraction Method for Visually-Rich Document. 1587–1598. 2 indexed citations
13.
He, Zewei, Yanpeng Cao, Jiangxin Yang, et al.. (2022). Single image super‐resolution based on progressive fusion of orientation‐aware features. Pattern Recognition. 133. 109038–109038. 33 indexed citations
14.
Wang, Xiaoqiang, Lei Zhu, Siliang Tang, et al.. (2022). Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images. IEEE Transactions on Image Processing. 31. 1107–1119. 19 indexed citations
15.
16.
Wu, Yu, Siliang Tang, Yi Yang, et al.. (2022). Learning to Learn by Jointly Optimizing Neural Architecture and Weights. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 129–138. 5 indexed citations
17.
Chen, Tao, et al.. (2021). CIL: Contrastive Instance Learning Framework for Distantly Supervised Relation Extraction. 6191–6200. 38 indexed citations
18.
Song, Jun, Siliang Tang, Jun Xiao, Fei Wu, & Zhongfei Zhang. (2016). LSTM-in-LSTM for generating long descriptions of images. Computational Visual Media. 2(4). 379–388. 20 indexed citations
19.
Dai, Hongliang, et al.. (2015). The ZJU-EDL System for Entity Discovery and Linking at TAC KBP 2015.. Theory and applications of categories. 1 indexed citations
20.
Tang, Siliang. (2005). Research of Military Software Quality Characteristic and Design Attribute. Jisuanji gongcheng. 1 indexed citations

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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