Ge Shi

741 total citations
36 papers, 418 citations indexed

About

Ge Shi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Ge Shi has authored 36 papers receiving a total of 418 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 15 papers in Computer Vision and Pattern Recognition and 6 papers in Information Systems. Recurrent topics in Ge Shi's work include Topic Modeling (10 papers), Multimodal Machine Learning Applications (7 papers) and Natural Language Processing Techniques (6 papers). Ge Shi is often cited by papers focused on Topic Modeling (10 papers), Multimodal Machine Learning Applications (7 papers) and Natural Language Processing Techniques (6 papers). Ge Shi collaborates with scholars based in China, Ireland and United States. Ge Shi's co-authors include Chong Feng, Lifang Wu, Abdallah Yousif, Arshad Ahmad, Heyan Huang, Xiao Liu, Meng Jian, Kan Li, Ruihai Dong and Lejian Liao and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Sensors.

In The Last Decade

Ge Shi

29 papers receiving 402 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ge Shi China 13 216 135 87 32 31 36 418
Guoming Lu China 11 202 0.9× 64 0.5× 50 0.6× 81 2.5× 18 0.6× 42 380
Aina Musdholifah Indonesia 10 220 1.0× 124 0.9× 49 0.6× 19 0.6× 34 1.1× 76 411
Chenglang Lu China 10 207 1.0× 82 0.6× 80 0.9× 46 1.4× 19 0.6× 20 407
Sungsu Lim South Korea 13 188 0.9× 74 0.5× 68 0.8× 119 3.7× 20 0.6× 49 455
Juntao Li China 15 423 2.0× 66 0.5× 120 1.4× 12 0.4× 7 0.2× 74 642
Xiaoqi Jiao China 4 675 3.1× 79 0.6× 296 3.4× 45 1.4× 43 1.4× 7 922
Abdullah Alourani Saudi Arabia 12 124 0.6× 98 0.7× 39 0.4× 103 3.2× 25 0.8× 45 428
Nisheeth Srivastava United States 8 103 0.5× 28 0.2× 23 0.3× 30 0.9× 16 0.5× 36 275
Yogesh Kumar Meena India 13 293 1.4× 124 0.9× 70 0.8× 43 1.3× 10 0.3× 56 468

Countries citing papers authored by Ge Shi

Since Specialization
Citations

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

Fields of papers citing papers by Ge Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ge Shi

This figure shows the co-authorship network connecting the top 25 collaborators of Ge Shi. A scholar is included among the top collaborators of Ge Shi 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 Ge Shi. Ge Shi 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.
Feng, Chong, et al.. (2025). Enhancing discriminative ability in multimodal LLMs: A contrastive learning approach for CT report generation. Information Fusion. 123. 103240–103240.
2.
Shi, Ge, et al.. (2025). EIKA: Explicit & Implicit Knowledge-Augmented Network for entity-aware sports video captioning. Expert Systems with Applications. 274. 126906–126906. 4 indexed citations
3.
4.
Jian, Meng, et al.. (2025). Geometric-Augmented Self-Distillation for Graph-Based Recommendation. ACM Transactions on Information Systems. 43(4). 1–23.
5.
Wu, Lifang, et al.. (2024). Learning Label Semantics for Weakly Supervised Group Activity Recognition. IEEE Transactions on Multimedia. 26. 6386–6397. 5 indexed citations
6.
Wu, Lifang, et al.. (2024). Learning to compose diversified prompts for image emotion classification. Computational Visual Media. 10(6). 1169–1183. 33 indexed citations
7.
Jian, Meng, et al.. (2024). Counterfactual Graph Convolutional Learning for Personalized Recommendation. ACM Transactions on Intelligent Systems and Technology. 15(4). 1–20. 2 indexed citations
8.
Huang, Heyan, et al.. (2023). Event Extraction With Dynamic Prefix Tuning and Relevance Retrieval. IEEE Transactions on Knowledge and Data Engineering. 35(10). 9946–9958. 12 indexed citations
9.
Shi, Ge, et al.. (2023). One for All: A Unified Generative Framework for Image Emotion Classification. IEEE Transactions on Circuits and Systems for Video Technology. 34(8). 7057–7068. 28 indexed citations
10.
Liu, Xiao, et al.. (2022). Dynamic Prefix-Tuning for Generative Template-based Event Extraction. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 5216–5228. 51 indexed citations
11.
Wu, Lifang, et al.. (2022). Sentiment Interaction Distillation Network for Image Sentiment Analysis. Applied Sciences. 12(7). 3474–3474. 4 indexed citations
12.
Shi, Ge, Feng Li, Lifang Wu, & Yukun Chen. (2022). Object-Level Visual-Text Correlation Graph Hashing for Unsupervised Cross-Modal Retrieval. Sensors. 22(8). 2921–2921. 4 indexed citations
13.
Wu, Lifang, et al.. (2022). Simple But Powerful, a Language-Supervised Method for Image Emotion Classification. IEEE Transactions on Affective Computing. 14(4). 3317–3331. 10 indexed citations
14.
Shi, Ge, et al.. (2022). Task-Aware Feature Composition for Few-Shot Relation Classification. Applied Sciences. 12(7). 3437–3437.
15.
16.
Shi, Ge, Chong Feng, Wenfu Xu, Lejian Liao, & Heyan Huang. (2020). Penalized multiple distribution selection method for imbalanced data classification. Knowledge-Based Systems. 196. 105833–105833. 13 indexed citations
17.
Ahmad, Arshad, Chong Feng, Ge Shi, & Abdallah Yousif. (2018). A survey on mining stack overflow: question and answering (Q&A) community. Data Technologies and Applications. 52(2). 190–247. 35 indexed citations
18.
Yan, Lei, et al.. (2016). Changes in T-lymphocytes in lung cancer patients after hyperthermic intraperitoneal chemotherapy or radiotherapy. Genetics and Molecular Research. 15(2). 3 indexed citations
19.
An, Tong, et al.. (2016). Thermal fatigue reliability analysis of PBGA with Sn63Pb37 solder joints. 76. 1104–1107. 7 indexed citations
20.

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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