Dian Shao

678 total citations
13 papers, 308 citations indexed

About

Dian Shao is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience and Artificial Intelligence. According to data from OpenAlex, Dian Shao has authored 13 papers receiving a total of 308 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Computer Vision and Pattern Recognition, 3 papers in Cognitive Neuroscience and 3 papers in Artificial Intelligence. Recurrent topics in Dian Shao's work include Human Pose and Action Recognition (6 papers), Multimodal Machine Learning Applications (4 papers) and EEG and Brain-Computer Interfaces (2 papers). Dian Shao is often cited by papers focused on Human Pose and Action Recognition (6 papers), Multimodal Machine Learning Applications (4 papers) and EEG and Brain-Computer Interfaces (2 papers). Dian Shao collaborates with scholars based in China, Hong Kong and Singapore. Dian Shao's co-authors include Bo Dai, Yue Zhao, Dahua Lin, Cewu Lu, Hao-Shu Fang, Yichen Xie, Weiping Ding, Shan Gao, Bowen Ji and Zuowei Zhang and has published in prestigious journals such as IEEE Transactions on Geoscience and Remote Sensing, IEEE Transactions on Neural Networks and Learning Systems and Micromachines.

In The Last Decade

Dian Shao

12 papers receiving 304 citations

Peers

Dian Shao

CVPR

AI

BE

HI

CSE

Bingbing Ni China

Zhan Tong China

Ming Shao United States

Zhaoyang Liu China

Chiara Plizzari Italy

Javed Imran India

Jogendra Nath Kundu India

Zhiwu Qing China

Toby Perrett United Kingdom

Preksha Pareek India

Bingbing Ni China

Dian Shao

392 ×1.6

CVPR

184 ×1.3

AI

67 ×1.3

BE

38 ×1.0

HI

15 ×0.8

CSE

Citations per year, relative to Dian Shao Dian Shao (= 1×) peers Bingbing Ni

Countries citing papers authored by Dian Shao

Since Specialization

Citations

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

Fields of papers citing papers by Dian Shao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dian Shao

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

All Works

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13 of 13 papers shown

1.

Shao, Dian, et al.. (2025). FinePhys: Fine-grained Human Action Generation by Explicitly Incorporating Physical Laws for Effective Skeletal Guidance. 1905–1916.

2.

Min, Lingtong, et al.. (2025). CM-YOLO: Context Modulated Representation Learning for Ship Detection. IEEE Transactions on Geoscience and Remote Sensing. 63. 1–14. 6 indexed citations

3.

Ning, Liangbo, Zuowei Zhang, Weiping Ding, Dian Shao, & Yining Zhu. (2025). Multilevel Distribution Alignment for Multisource Universal Domain Adaptation. IEEE Transactions on Neural Networks and Learning Systems. 36(9). 17365–17379. 8 indexed citations

4.

Chen, Haodong, et al.. (2025). SeFAR: Semi-supervised Fine-grained Action Recognition with Temporal Perturbation and Learning Stabilization. Proceedings of the AAAI Conference on Artificial Intelligence. 39(4). 3833–3841. 2 indexed citations

5.

Zheng, Mingzhe, et al.. (2024). FineCLIPER: Multi-modal Fine-grained CLIP for Dynamic Facial Expression Recognition with AdaptERs. 2301–2310. 6 indexed citations

6.

Liu, Chang, et al.. (2024). Effective Rotate: Learning Rotation-Robust Prototype for Aerial Object Detection. IEEE Transactions on Geoscience and Remote Sensing. 62. 1–14. 4 indexed citations

7.

Ji, Bowen, et al.. (2023). A Methodology for Enhancing SSVEP Features Using Adaptive Filtering Based on the Spatial Distribution of EEG Signals. Micromachines. 14(5). 976–976. 6 indexed citations

8.

Wang, Minghao, Xuanqi Wang, Xiaoli You, et al.. (2023). Smart epidermal electrophysiological electrodes: Materials, structures, and algorithms. Nanotechnology and Precision Engineering. 6(4). 7 indexed citations

9.

Sha, Meng, et al.. (2023). Tracking without Label: Unsupervised Multiple Object Tracking via Contrastive Similarity Learning. 16218–16227. 4 indexed citations

10.

Fang, Hao-Shu, Yichen Xie, Dian Shao, Yong–Lu Li, & Cewu Lu. (2021). DecAug: Augmenting HOI Detection via Decomposition. Proceedings of the AAAI Conference on Artificial Intelligence. 35(2). 1300–1308. 3 indexed citations

11.

Fang, Hao-Shu, Yichen Xie, Dian Shao, & Cewu Lu. (2021). DIRV: Dense Interaction Region Voting for End-to-End Human-Object Interaction Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 35(2). 1291–1299. 35 indexed citations

12.

Shao, Dian, Yue Zhao, Bo Dai, & Dahua Lin. (2020). FineGym: A Hierarchical Video Dataset for Fine-Grained Action Understanding. The HKU Scholars Hub (University of Hong Kong). 2613–2622. 181 indexed citations

13.

Shao, Dian, Yue Zhao, Bo Dai, & Dahua Lin. (2020). Intra- and Inter-Action Understanding via Temporal Action Parsing. The HKU Scholars Hub (University of Hong Kong). 727–736. 46 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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