Lin Ma

15.5k total citations · 3 hit papers
270 papers, 7.8k citations indexed

About

Lin Ma is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Safety, Risk, Reliability and Quality. According to data from OpenAlex, Lin Ma has authored 270 papers receiving a total of 7.8k indexed citations (citations by other indexed papers that have themselves been cited), including 146 papers in Computer Vision and Pattern Recognition, 50 papers in Artificial Intelligence and 33 papers in Safety, Risk, Reliability and Quality. Recurrent topics in Lin Ma's work include Image and Video Quality Assessment (46 papers), Multimodal Machine Learning Applications (32 papers) and Advanced Image Processing Techniques (32 papers). Lin Ma is often cited by papers focused on Image and Video Quality Assessment (46 papers), Multimodal Machine Learning Applications (32 papers) and Advanced Image Processing Techniques (32 papers). Lin Ma collaborates with scholars based in China, Hong Kong and Australia. Lin Ma's co-authors include Wei Liu, King Ngi Ngan, Songnan Li, Wenhan Luo, Wenhao Jiang, Jingwen Wang, Zhangkai Ni, Zequn Jie, Wenqi Ren and Joseph Mathew and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Transactions on Pattern Analysis and Machine Intelligence.

In The Last Decade

Lin Ma

253 papers receiving 7.6k citations

Hit Papers

Low-Light Image Enhancement via a Deep Hybrid Network 2019 2026 2021 2023 2019 2020 2021 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lin Ma China 47 5.6k 1.5k 1.3k 487 448 270 7.8k
Junchi Yan China 44 5.0k 0.9× 2.4k 1.7× 1.3k 1.0× 233 0.5× 78 0.2× 267 8.7k
Shiliang Sun China 43 2.7k 0.5× 3.7k 2.5× 598 0.5× 581 1.2× 73 0.2× 197 7.2k
Dit‐Yan Yeung Hong Kong 44 5.4k 1.0× 4.1k 2.8× 1.2k 1.0× 257 0.5× 186 0.4× 152 9.5k
Gwanggil Jeon South Korea 46 3.4k 0.6× 1.7k 1.2× 1.8k 1.4× 479 1.0× 190 0.4× 533 8.8k
Yiu‐ming Cheung Hong Kong 43 3.5k 0.6× 2.7k 1.8× 704 0.6× 333 0.7× 60 0.1× 362 6.8k
Tommy W. S. Chow Hong Kong 49 2.0k 0.4× 2.8k 1.9× 335 0.3× 2.3k 4.8× 217 0.5× 275 7.5k
Pabitra Mitra India 35 1.6k 0.3× 2.5k 1.7× 360 0.3× 199 0.4× 85 0.2× 186 5.4k
Xianbin Cao China 46 2.3k 0.4× 1.2k 0.8× 365 0.3× 247 0.5× 324 0.7× 250 7.1k
Rui Xu China 17 2.0k 0.4× 2.5k 1.7× 474 0.4× 211 0.4× 84 0.2× 77 5.4k
Thierry Denœux France 44 1.2k 0.2× 4.0k 2.8× 289 0.2× 664 1.4× 122 0.3× 180 6.6k

Countries citing papers authored by Lin Ma

Since Specialization
Citations

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

Fields of papers citing papers by Lin Ma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lin Ma

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

All Works

20 of 20 papers shown
1.
Zhao, Zhixin, Yifei Zhou, Lin Ma, et al.. (2024). Object Detection and Information Perception by Fusing YOLO-SCG and Point Cloud Clustering. Sensors. 24(16). 5357–5357. 2 indexed citations
2.
Wang, Xu, et al.. (2024). Weakly-Supervised 3D Scene Graph Generation via Visual-Linguistic Assisted Pseudo-Labeling. IEEE Transactions on Multimedia. 26. 11164–11175. 1 indexed citations
3.
Hu, Kang‐Di, et al.. (2024). Persulfidation and phosphorylation of transcription factor SlWRKY6 differentially regulate tomato fruit ripening. PLANT PHYSIOLOGY. 196(1). 210–227. 22 indexed citations
4.
Ma, Lin, et al.. (2022). A Hydrogen-Sulfide-Repressed Methionine Synthase SlMS1 Acts as a Positive Regulator for Fruit Ripening in Tomato. International Journal of Molecular Sciences. 23(20). 12239–12239. 7 indexed citations
5.
Li, Jinlong, Zequn Jie, Xu Wang, et al.. (2022). Weakly Supervised Semantic Segmentation Via Progressive Patch Learning. IEEE Transactions on Multimedia. 25. 1686–1699. 17 indexed citations
6.
Zhang, Pingping, Xu Wang, Lin Ma, et al.. (2021). Progressive Point Cloud Upsampling via Differentiable Rendering. IEEE Transactions on Circuits and Systems for Video Technology. 31(12). 4673–4685. 25 indexed citations
7.
Kang, Dongwoo & Lin Ma. (2021). Real-Time Eye Tracking for Bare and Sunglasses-Wearing Faces for Augmented Reality 3D Head-Up Displays. IEEE Access. 9. 125508–125522. 14 indexed citations
8.
Zhang, Kaihao, Wenhan Luo, Lin Ma, Wenqi Ren, & Hongdong Li. (2021). Disentangled Feature Networks for Facial Portrait and Caricature Generation. IEEE Transactions on Multimedia. 24. 1378–1388. 4 indexed citations
9.
Ma, Lin, et al.. (2021). Fostering Team‐Level Idea Implementation: Leader's Upward Exchange Relationship as a Key Facilitator. British Journal of Management. 33(1). 519–535. 4 indexed citations
10.
Liu, Wen, Zhixin Piao, Zhi Tu, et al.. (2021). Liquid Warping GAN With Attention: A Unified Framework for Human Image Synthesis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(9). 5115–5133. 29 indexed citations
11.
Wang, Xu, et al.. (2020). Multi-Exposure Decomposition-Fusion Model for High Dynamic Range Image Saliency Detection. IEEE Transactions on Circuits and Systems for Video Technology. 30(12). 4409–4420. 19 indexed citations
12.
Ni, Zhangkai, Wenhan Yang, Shiqi Wang, Lin Ma, & Sam Kwong. (2020). Towards Unsupervised Deep Image Enhancement With Generative Adversarial Network. IEEE Transactions on Image Processing. 29. 9140–9151. 102 indexed citations
13.
Ren, Wenqi, Sifei Liu, Lin Ma, et al.. (2019). Low-Light Image Enhancement via a Deep Hybrid Network. IEEE Transactions on Image Processing. 28(9). 4364–4375. 370 indexed citations breakdown →
14.
Ma, Lin, Wenhao Jiang, Zequn Jie, & Xu Wang. (2019). Bidirectional image-sentence retrieval by local and global deep matching. Neurocomputing. 345. 36–44. 23 indexed citations
15.
Ren, Wenqi, Jingang Zhang, Xiangyu Xu, et al.. (2018). Deep Video Dehazing With Semantic Segmentation. IEEE Transactions on Image Processing. 28(4). 1895–1908. 152 indexed citations
16.
Ren, Wenqi, Jiawei Zhang, Lin Ma, et al.. (2018). Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation. Neural Information Processing Systems. 31. 297–307. 43 indexed citations
17.
Pavlo, Andrew, Joy Arulraj, Haibin Lin, et al.. (2017). Self-Driving Database Management Systems.. Conference on Innovative Data Systems Research. 130 indexed citations
18.
Ma, Lin, et al.. (2013). Study on Equipment Health Management System Modelling Based on DoDAF. SHILAP Revista de lepidopterología. 1 indexed citations
19.
Kyan, Matthew, et al.. (2011). A visual saliency modulated just noticeable distortion profile for image watermarking. European Signal Processing Conference. 2039–2043. 5 indexed citations
20.
Yang, Hongyu, Joseph Mathew, & Lin Ma. (2003). Vibration feature extraction techniques for fault diagnosis of rotating machinery : a literature survey. The Science of The Total Environment. 554-555. 253–8. 38 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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