Miaojing Shi

2.7k total citations · 1 hit paper
56 papers, 1.2k citations indexed

About

Miaojing Shi is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, Miaojing Shi has authored 56 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Computer Vision and Pattern Recognition, 20 papers in Artificial Intelligence and 8 papers in Biomedical Engineering. Recurrent topics in Miaojing Shi's work include Multimodal Machine Learning Applications (11 papers), Advanced Image and Video Retrieval Techniques (11 papers) and Image Retrieval and Classification Techniques (9 papers). Miaojing Shi is often cited by papers focused on Multimodal Machine Learning Applications (11 papers), Advanced Image and Video Retrieval Techniques (11 papers) and Image Retrieval and Classification Techniques (9 papers). Miaojing Shi collaborates with scholars based in China, United Kingdom and France. Miaojing Shi's co-authors include Chao Xu, Qijun Chen, Zhaohui Yang, Yuting Liu, Qijun Zhao, Xiaofang Wang, Fangyun Wei, Guoqi Li, Yue Gao and Yu Du and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Proceedings of the IEEE.

In The Last Decade

Miaojing Shi

51 papers receiving 1.2k 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.

Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model

2022 198 citations Fangyun Wei, Miaojing Shi et al. profile →

Peers

Miaojing Shi

CVPR

AI

SRRQ

CAM

MT

Li He China

Pengcheng Wu China

Yuan Dong China

Tianshui Chen China

Lei Huang China

P. Viswanath India

Changsheng Li China

Jianhua Zou China

Boaz Lerner Israel

Li He China

Miaojing Shi

706 ×0.9

CVPR

279 ×0.5

AI

16 ×0.2

SRRQ

28 ×0.4

CAM

189 ×2.8

MT

Citations per year, relative to Miaojing Shi Miaojing Shi (= 1×) peers Li He

Countries citing papers authored by Miaojing Shi

Since Specialization

Citations

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

Fields of papers citing papers by Miaojing Shi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Miaojing Shi

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

Brown, Neil C. C., et al.. (2025). How Consistent Are Humans When Grading Programming Assignments?. ACM Transactions on Computing Education. 25(4). 1–37.

2.

Puyol‐Antón, Esther, et al.. (2025). An investigation into the causes of race bias in artificial intelligence–based cine cardiac magnetic resonance segmentation. European Heart Journal - Digital Health. 6(3). 350–358. 2 indexed citations

3.

Brown, Neil C. C., et al.. (2025). Menagerie: A Dataset of Graded Programming Assignments. 1547–1548. 1 indexed citations

4.

Shi, Miaojing, et al.. (2025). Bootstrapping Vision-Language Models for Frequency-Centric Self-Supervised Remote Physiological Measurement. International Journal of Computer Vision. 133(7). 4112–4133. 2 indexed citations

5.

Wen, Bo, et al.. (2025). Intelligent Virtual Dental Implant Placement via 3D Segmentation Strategy. Journal of Dental Research. 104(11). 1208–1217. 1 indexed citations

6.

Zhou, Zijian, et al.. (2025). CholecInstanceSeg: A Tool Instance Segmentation Dataset for Laparoscopic Surgery. Scientific Data. 12(1). 825–825. 1 indexed citations

7.

Budd, Charlie, et al.. (2025). SegMatch: semi-supervised surgical instrument segmentation. Scientific Reports. 15(1). 14042–14042.

8.

Vercauteren, Tom, et al.. (2025). Multitask learning in minimally invasive surgical vision: A review. Medical Image Analysis. 101. 103480–103480. 2 indexed citations

9.

Wang, Hanli, et al.. (2024). Multi-Modal Large Language Model Enhanced Pseudo 3D Perception Framework for Visual Commonsense Reasoning. IEEE Transactions on Circuits and Systems for Video Technology. 34(11). 11682–11694. 2 indexed citations

10.

He, Shibo, et al.. (2024). Semantic-DARTS: Elevating Semantic Learning for Mobile Differentiable Architecture Search. IEEE Internet of Things Journal. 12(2). 1673–1687. 1 indexed citations

11.

Shi, Miaojing, et al.. (2024). Memory-Based Contrastive Learning with Optimized Sampling for Incremental Few-Shot Semantic Segmentation. 1–5.

12.

Spratling, Michael, et al.. (2024). When Multitask Learning Meets Partial Supervision: A Computer Vision Review. Proceedings of the IEEE. 112(6). 516–543. 4 indexed citations

13.

Liu, Han, Jinghao Duan, Miaojing Shi, et al.. (2024). Intelligently Quantifying the Entire Irregular Dental Structure. Journal of Dental Research. 103(4). 378–387. 1 indexed citations

14.

Shi, Miaojing, et al.. (2024). Enhancing space–time video super-resolution via spatial–temporal feature interaction. Neural Networks. 184. 107033–107033.

15.

Amirkolaee, Hamed Amini, Miaojing Shi, & Mark Mulligan. (2023). TreeFormer: A Semi-Supervised Transformer-Based Framework for Tree Counting From a Single High-Resolution Image. IEEE Transactions on Geoscience and Remote Sensing. 61. 1–15. 18 indexed citations

16.

Shi, Miaojing, et al.. (2023). Boosting Zero-Shot Learning via Contrastive Optimization of Attribute Representations. IEEE Transactions on Neural Networks and Learning Systems. 35(11). 16706–16719. 12 indexed citations

17.

Brown, Neil C. C., et al.. (2023). Automated Grading and Feedback Tools for Programming Education: A Systematic Review. ACM Transactions on Computing Education. 24(1). 1–43. 60 indexed citations

18.

Deng, Jiankang, et al.. (2023). Domain-General Crowd Counting in Unseen Scenarios. Proceedings of the AAAI Conference on Artificial Intelligence. 37(1). 561–570. 16 indexed citations

19.

Li, Shugang, et al.. (2019). Select the potential brand spokesperson on social network based on the smart link prediction. Journal of Intelligent & Fuzzy Systems. 37(1). 625–634. 1 indexed citations

20.

Shi, Miaojing, Guozheng Li, Fufeng Li, & Chao Xu. (2012). Computer Science Education (ICCSE), 2012 7th International Conference on. 32 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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