Ming Gong

6.0k total citations · 2 hit papers
51 papers, 2.7k citations indexed

About

Ming Gong is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Ming Gong has authored 51 papers receiving a total of 2.7k indexed citations (citations by other indexed papers that have themselves been cited), including 35 papers in Artificial Intelligence, 19 papers in Computer Vision and Pattern Recognition and 7 papers in Information Systems. Recurrent topics in Ming Gong's work include Topic Modeling (29 papers), Natural Language Processing Techniques (27 papers) and Multimodal Machine Learning Applications (14 papers). Ming Gong is often cited by papers focused on Topic Modeling (29 papers), Natural Language Processing Techniques (27 papers) and Multimodal Machine Learning Applications (14 papers). Ming Gong collaborates with scholars based in China, United States and Canada. Ming Gong's co-authors include Daxin Jiang, Nan Duan, Linjun Shou, Ming Zhou, Duyu Tang, Daya Guo, Zhangyin Feng, Bing Qin, Ting Liu and Xiaocheng Feng and has published in prestigious journals such as SHILAP Revista de lepidopterología, Medicine and Remote Sensing.

In The Last Decade

Ming Gong

49 papers receiving 2.6k citations

Hit Papers

align trajectories sort by overperformance

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

CodeBERT: A Pre-Trained Model for Programming and Natural Languages

2020 1.4k citations Daya Guo, Duyu Tang et al. profile →
Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training

2020 479 citations Gen Li, Nan Duan et al. Proceedings of the AAAI Conference on Artificial Intelligence profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ming Gong China	18	1.7k	1.1k	682	521	368	51	2.7k
Lili Mou China	18	1.8k 1.1×	617 0.6×	356 0.5×	269 0.5×	222 0.6×	59	2.2k
Xinyu Xing United States	22	942 0.6×	742 0.7×	151 0.2×	261 0.5×	750 2.0×	103	1.9k
Yansong Feng China	25	1.8k 1.1×	494 0.4×	564 0.8×	105 0.2×	258 0.7×	102	2.4k
Jianwen Su United States	28	1.5k 0.9×	1.5k 1.4×	160 0.2×	92 0.2×	668 1.8×	132	2.8k
Ken Arnold United Kingdom	13	663 0.4×	480 0.4×	160 0.2×	196 0.4×	80 0.2×	48	1.7k
Guozhu Meng China	21	439 0.3×	844 0.8×	107 0.2×	601 1.2×	833 2.3×	48	1.6k
Vincenzo Deufemia Italy	20	458 0.3×	516 0.5×	176 0.3×	228 0.4×	118 0.3×	92	975
Yutao Ma China	22	452 0.3×	1.1k 1.0×	119 0.2×	356 0.7×	82 0.2×	78	1.4k
Robin Laney United Kingdom	18	455 0.3×	749 0.7×	170 0.2×	190 0.4×	282 0.8×	72	1.3k
Min Yang China	22	1.0k 0.6×	394 0.4×	372 0.5×	85 0.2×	78 0.2×	90	1.5k

Countries citing papers authored by Ming Gong

Since Specialization

Citations

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

Fields of papers citing papers by Ming Gong

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ming Gong

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Wu, Ning, et al.. (2025). Ploutos: Towards Explainable Stock Movement Prediction with Financial Large Language Model. 490–499. 1 indexed citations

Gong, Ming, et al.. (2024). YOLO-based GNN for multi-person pose estimation. 20–20. 2 indexed citations

Chen, Nuo, Linjun Shou, Jian Pei, et al.. (2023). Alleviating Over-smoothing for Unsupervised Sentence Representation. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 3552–3566. 3 indexed citations

Chen, Nuo, Linjun Shou, Ming Gong, et al.. (2023). Structural Contrastive Pretraining for Cross-Lingual Comprehension. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 2042–2057. 1 indexed citations

Shao, Bo, et al.. (2023). WIERT: Web Information Extraction via Render Tree. Proceedings of the AAAI Conference on Artificial Intelligence. 37(11). 13166–13173. 3 indexed citations

Li, Xiaoquan, Stephan Weiss, Yijun Yan, et al.. (2023). Siamese Residual Neural Network for Musical Shape Evaluation in Piano Performance Assessment. Open Access Institutional Repository at Robert Gordon University (Robert Gordon University). 1–5. 1 indexed citations

Chen, Nuo, Linjun Shou, Ming Gong, Jian Pei, & Daxin Jiang. (2022). Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1909–1923. 4 indexed citations

Guo, Daya, Duyu Tang, Qinliang Su, et al.. (2021). Syntax-Enhanced Pre-trained Model. 5412–5422. 20 indexed citations

Guo, Yingmei, Linjun Shou, Jian Pei, et al.. (2021). Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 3226–3237. 1 indexed citations

10.

Liu, Dayiheng, Yu Yan, Yeyun Gong, et al.. (2021). GLGE: A New General Language Generation Evaluation Benchmark. 408–420. 32 indexed citations

11.

Gong, Ming, et al.. (2021). Assessment on typhoon prevention capability of fishing port based on numerical model calculations. IOP Conference Series Earth and Environmental Science. 861(7). 72007–72007. 2 indexed citations

12.

Luo, Huaishao, Yu Shi, Ming Gong, Linjun Shou, & Tianrui Li. (2020). MaP: A Matrix-based Prediction Approach to Improve Span Extraction in Machine Reading Comprehension. 687–695. 3 indexed citations

13.

Qin, Yong, et al.. (2020). Railway Train Device Fault Causality Model Based on Knowledge Graph. 385–390. 7 indexed citations

14.

Wang, Xuguang, Linjun Shou, Ming Gong, Nan Duan, & Daxin Jiang. (2020). No Answer is Better Than Wrong Answer: A Reflection Model for Document Level Machine Reading Comprehension. 4141–4150. 4 indexed citations

15.

Li, Gen, Nan Duan, Yuejian Fang, Ming Gong, & Daxin Jiang. (2020). Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training. Proceedings of the AAAI Conference on Artificial Intelligence. 34(7). 11336–11344. 479 indexed citations breakdown →

16.

Gong, Ming, et al.. (2019). Finite Element Model Updating of Structures Using Deep Neural Network. 39(1). 147–154. 2 indexed citations

17.

Huang, Haoyang, Yaobo Liang, Nan Duan, et al.. (2019). Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks. 2485–2494. 104 indexed citations

18.

Mander, Luke, et al.. (2017). The Geometry of Large Tundra Lakes Observed in Historical Maps and Satellite Images. Remote Sensing. 9(10). 1072–1072. 8 indexed citations

19.

Gong, Ming, et al.. (2013). A Power Electronic Fault Diagnosis Method Based on Bond Graph Model. SHILAP Revista de lepidopterología. 1 indexed citations

20.

Bailes, Paul A., et al.. (2002). GRIT-an extended REFINE for more executable specifications. 123–132. 2 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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