Jing Liu

7.0k total citations · 2 hit papers
334 papers, 4.1k citations indexed

About

Jing Liu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Jing Liu has authored 334 papers receiving a total of 4.1k indexed citations (citations by other indexed papers that have themselves been cited), including 135 papers in Computer Vision and Pattern Recognition, 92 papers in Artificial Intelligence and 62 papers in Information Systems. Recurrent topics in Jing Liu's work include Advanced Image Processing Techniques (32 papers), Image Enhancement Techniques (26 papers) and Advanced Image and Video Retrieval Techniques (24 papers). Jing Liu is often cited by papers focused on Advanced Image Processing Techniques (32 papers), Image Enhancement Techniques (26 papers) and Advanced Image and Video Retrieval Techniques (24 papers). Jing Liu collaborates with scholars based in China, United States and Singapore. Jing Liu's co-authors include Guangtao Zhai, Yuting Su, Xiaokang Yang, Xiongkuo Min, Ji Zhang, Zhicheng Li, Qihua Li, Yinsheng Chen, Jiangwei Lao and Peiguang Jing and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and Water Resources Research.

In The Last Decade

Jing Liu

295 papers receiving 4.0k 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.

A Deep Learning-Based Radiomics Model for Prediction of Survival in Glioblastoma Multiforme

2017 461 citations Jing Liu et al. profile →
Blind Quality Assessment Based on Pseudo-Reference Image

2017 330 citations Jing Liu, Xiaokang Yang et al. IEEE Transactions on Multimedia profile →

Peers

Jing Liu

CVPR

AI

IS

CNC

MT

Shuai Liu China

Ruili Wang New Zealand

Vinod Kumar India

Quanming Yao China

Adel Elmaghraby United States

Yang Gao China

Qinfeng Shi Australia

Ying Chen China

Jianping Li China

Huaxiang Zhang China

Shuai Liu China

Jing Liu

2.0k ×1.1

CVPR

2.3k ×2.1

AI

694 ×1.3

IS

289 ×0.6

CNC

410 ×0.9

MT

Citations per year, relative to Jing Liu Jing Liu (= 1×) peers Shuai Liu

Countries citing papers authored by Jing Liu

Since Specialization

Citations

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

Fields of papers citing papers by Jing Liu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jing Liu

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

Liu, Jing, et al.. (2025). BE-SGGAN: Content-aware bit-depth enhancement by semantic guided GAN. Digital Signal Processing. 160. 105030–105030. 1 indexed citations

2.

Liu, Jing, et al.. (2025). Retinal features as predictive indicators for high myopia: insights from explainable multi-machine learning models. Frontiers in Bioengineering and Biotechnology. 13. 1609639–1609639.

3.

Xiang, Yang, et al.. (2025). A Sandwich-Type Impedimetric Immunosensor for the Detection of Tau-441 Biomarker. Bioengineering. 12(8). 805–805. 1 indexed citations

4.

Sun, Yifei, Zhanghao Chen, Hao Zheng, et al.. (2025). BS-LDM: Effective B one S uppression in High-Resolution Chest X-Ray Images With Conditional L atent D iffusion M odels. IEEE Journal of Biomedical and Health Informatics. 30(1). 376–387.

5.

An, Qiang, et al.. (2025). Unraveling the role of moisture recycling in water consumption across different ecosystems in China. Journal of Hydrology. 662. 134020–134020.

6.

Liu, Jing, et al.. (2024). How do health content creators perform well? An integration research of short video and livestream behaviors. Frontiers in Public Health. 12. 1446247–1446247. 3 indexed citations

7.

Zhuang, Lei, et al.. (2024). Fast prediction of three-dimensional indoor flow fields by a reduced dimensional deep-learning approach. Building and Environment. 270. 112517–112517. 4 indexed citations

8.

An, Qiang, et al.. (2024). Crops Feed Rain to Drylands in Northwest China. Earth s Future. 12(10). 9 indexed citations

9.

Liu, Jing, et al.. (2024). Underwater object detection by integrating YOLOv8 and efficient transformer. Journal of Electronic Imaging. 33(4). 2 indexed citations

10.

Wang, Weining, et al.. (2023). Reparameterizing and dynamically quantizing image features for image generation. Pattern Recognition. 146. 109962–109962. 8 indexed citations

11.

Wang, Xuchun, Yu Cui, Jing Liu, et al.. (2023). A study on the preparation conditions of lidocaine microemulsion based on multi-objective genetic algorithm. Frontiers in Pharmacology. 14. 1272454–1272454. 2 indexed citations

12.

Shen, Yi, et al.. (2023). The Meshless Direct Simulation Monte Carlo method. Journal of Computational Physics. 481. 112039–112039. 2 indexed citations

13.

Liu, Jing, et al.. (2023). Efficient Spatio-Temporal Video Grounding with Semantic-Guided Feature Decomposition. 4867–4876. 6 indexed citations

14.

Huang, Xiangdong, et al.. (2023). MALN: Multimodal Adversarial Learning Network for Conversational Emotion Recognition. IEEE Transactions on Circuits and Systems for Video Technology. 33(11). 6965–6980. 19 indexed citations

15.

Liu, Jing, Ziwen Yang, Yuting Su, & Xiaokang Yang. (2022). TANet: Target Attention Network for Video Bit-Depth Enhancement. IEEE Transactions on Multimedia. 24. 4212–4223. 4 indexed citations

16.

Rapp, Sebastian, et al.. (2020). Recognition of track defects through measured acceleration using a recurrent neural network. International Journal of Computational Methods and Experimental Measurements. 8(3). 270–280. 4 indexed citations

17.

Liu, Jing, et al.. (2020). SODA²:Salient Object Detection With Structure-Adaptive & Scale-Adaptive Receptive Field. IEEE Access. 8. 204160–204172. 5 indexed citations

18.

Su, Yuting, Xu Bai, Wu Li, et al.. (2018). Graph regularized low-rank tensor representation for feature selection. Journal of Visual Communication and Image Representation. 56. 234–244. 4 indexed citations

19.

Qin, Yong, et al.. (2013). Research on the Prediction Model for the Security Situation of Metro Station Based on PSO/SVM. 5(4). 237–244. 3 indexed citations

20.

Liu, Jing, Quan Wang, Chin-Yew Lin, & Hsiao-Wuen Hon. (2013). Question Difficulty Estimation in Community Question Answering Services. 85–90. 23 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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