Yangdong Ye

2.5k total citations · 1 hit paper
126 papers, 1.7k citations indexed

About

Yangdong Ye is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Yangdong Ye has authored 126 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 73 papers in Artificial Intelligence, 63 papers in Computer Vision and Pattern Recognition and 28 papers in Information Systems. Recurrent topics in Yangdong Ye's work include Advanced Image and Video Retrieval Techniques (24 papers), Video Surveillance and Tracking Methods (23 papers) and Recommender Systems and Techniques (22 papers). Yangdong Ye is often cited by papers focused on Advanced Image and Video Retrieval Techniques (24 papers), Video Surveillance and Tracking Methods (23 papers) and Recommender Systems and Techniques (22 papers). Yangdong Ye collaborates with scholars based in China, United Kingdom and United States. Yangdong Ye's co-authors include Shizhe Hu, Xiaoqiang Yan, Zenglin Shi, Bin Wu, Yiqiao Mao, Hui Yu, Zhengzheng Lou, Le Zhang, Guoyan Zheng and Yun Liu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Molecular Sciences and IEEE Transactions on Image Processing.

In The Last Decade

Yangdong Ye

112 papers receiving 1.7k citations

Hit Papers

align trajectories

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.

Deep multi-view learning methods: A review

2021 199 citations Xiaoqiang Yan, Shizhe Hu et al. Neurocomputing profile →

Peers

Yangdong Ye

AI

CVPR

IS

SRRQ

MT

Jie Hu China

Yingjie Xia China

Xin Yang China

Fazhi He China

Shuyin Xia China

Yifan Feng China

Yuhong Li China

Yi Zhou China

Jun Liang China

Minnan Luo China

Jie Hu China

Yangdong Ye

520 ×0.5

AI

518 ×0.5

CVPR

185 ×0.6

IS

36 ×0.3

SRRQ

69 ×0.7

MT

Citations per year, relative to Yangdong Ye Yangdong Ye (= 1×) peers Jie Hu

Countries citing papers authored by Yangdong Ye

Since Specialization

Citations

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

Fields of papers citing papers by Yangdong Ye

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yangdong Ye

This figure shows the co-authorship network connecting the top 25 collaborators of Yangdong Ye. A scholar is included among the top collaborators of Yangdong Ye 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 Yangdong Ye. Yangdong Ye 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

1.

Mao, Yiqiao, et al.. (2025). ProNet: A contrastive propagation network for multimodal lifelong clustering. Knowledge-Based Systems. 317. 113349–113349.

2.

Wu, Bin, et al.. (2025). Disentangled Graph Contrastive Learning for Socially-Aware Next-Item Recommendation. IEEE Transactions on Big Data. 11(6). 3197–3211.

3.

Yan, Xiaoqiang, et al.. (2024). Differentiable self-supervised clustering with intrinsic interpretability. Neural Networks. 179. 106542–106542.

4.

Mao, Yiqiao, et al.. (2024). Memory-aware continual learning with multi-modal social media streams for unsupervised disaster classification. Advanced Engineering Informatics. 62. 102654–102654. 3 indexed citations

5.

Ye, Yangdong, et al.. (2024). Meta-collaboration-based semantic contrast for inductive knowledge representation learning. Expert Systems with Applications. 261. 125421–125421. 1 indexed citations

6.

Mao, Yiqiao, Xiaoqiang Yan, Shizhe Hu, & Yangdong Ye. (2024). Contrastive cross-modal clustering with twin network. Pattern Recognition. 155. 110645–110645. 1 indexed citations

7.

Ye, Yangdong, Yanan Wang, Xiaoqing Wu, et al.. (2024). Identification and candidate analysis of a new brown planthopper resistance locus in an Indian landrace of rice, paedai kalibungga. Molecular Breeding. 44(7). 45–45. 2 indexed citations

8.

Wu, Bin, et al.. (2023). Graph-coupled time interval network for sequential recommendation. Information Sciences. 648. 119510–119510. 9 indexed citations

9.

Liu, Dong, et al.. (2023). Self-supervised temporal autoencoder for egocentric action segmentation. Engineering Applications of Artificial Intelligence. 126. 107092–107092. 2 indexed citations

10.

Mao, Yiqiao, et al.. (2023). A Bottleneck Network with Light Attention for Multimodal Clustering. Knowledge-Based Systems. 280. 111037–111037.

11.

Wang, Youwei, et al.. (2023). Deep purified feature mining model for joint named entity recognition and relation extraction. Information Processing & Management. 60(6). 103511–103511. 11 indexed citations

12.

Yan, Xiaoqiang, et al.. (2023). Mutual Boost Network for attributed graph clustering. Expert Systems with Applications. 229. 120479–120479. 14 indexed citations

13.

Wu, Bin, et al.. (2023). Modeling Product’s Visual and Functional Characteristics for Recommender Systems (Extended Abstract). 3837–3838.

14.

Wu, Bin, et al.. (2022). EAGCN: An Efficient Adaptive Graph Convolutional Network for Item Recommendation in Social Internet of Things. IEEE Internet of Things Journal. 9(17). 16386–16401. 42 indexed citations

15.

Yan, Xiaoqiang, et al.. (2019). CMIB: Unsupervised Image Object Categorization in Multiple Visual Contexts. IEEE Transactions on Industrial Informatics. 16(6). 3974–3986. 12 indexed citations

16.

Ye, Yangdong, et al.. (2017). Collective Density Clustering for Coherent Motion Detection. IEEE Transactions on Multimedia. 20(6). 1418–1431. 11 indexed citations

17.

Yan, Xiaoqiang, et al.. (2016). Unsupervised human action categorization with consensus information bottleneck method. International Joint Conference on Artificial Intelligence. 2245–2251. 10 indexed citations

18.

Xu, Mingliang, et al.. (2015). miSFM: On combination of Mutual Information and Social Force Model towards simulating crowd evacuation. Neurocomputing. 168. 529–537. 40 indexed citations

19.

Lou, Zhengzheng, Yangdong Ye, & Xiaoqiang Yan. (2013). The multi-feature information bottleneck with application to unsupervised image categorization. International Joint Conference on Artificial Intelligence. 1508–1515. 11 indexed citations

20.

Cai, Guoqiang, et al.. (2006). A multi-agent model of railway intelligent safety guarantee system.. Computational intelligence. 138–143. 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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