Liangzhi Li

2.0k total citations · 2 hit papers
39 papers, 1.3k citations indexed

About

Liangzhi Li is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Liangzhi Li has authored 39 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Computer Vision and Pattern Recognition, 14 papers in Artificial Intelligence and 4 papers in Electrical and Electronic Engineering. Recurrent topics in Liangzhi Li's work include Advanced Neural Network Applications (9 papers), Multimodal Machine Learning Applications (6 papers) and Domain Adaptation and Few-Shot Learning (6 papers). Liangzhi Li is often cited by papers focused on Advanced Neural Network Applications (9 papers), Multimodal Machine Learning Applications (6 papers) and Domain Adaptation and Few-Shot Learning (6 papers). Liangzhi Li collaborates with scholars based in Japan, China and Singapore. Liangzhi Li's co-authors include Mianxiong Dong, Kaoru Ota, Hajime Nagahara, Yuta Nakashima, Ryo Kawasaki, Manisha Verma, Bowen Wang, Manisha Verma, Yuta Nakashima and Bin Qiao and has published in prestigious journals such as Water Research, Scientific Reports and IEEE Access.

In The Last Decade

Liangzhi Li

34 papers receiving 1.2k citations

Hit Papers

Deep Learning for Smart Industry: Efficient Manufacture I... 2018 2026 2020 2023 2018 2020 100 200 300
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.

  1. Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing
    2018 343 citations Liangzhi Li, Kaoru Ota et al. IEEE Transactions on Industrial Informatics profile →
  2. IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
    2020 218 citations Liangzhi Li, Manisha Verma et al. profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Liangzhi Li Japan 14 445 268 263 251 226 39 1.3k
Xiaoguang Zhou China 22 190 0.4× 161 0.6× 67 0.3× 116 0.5× 196 0.9× 116 1.4k
Abiodun Musa Aibinu Nigeria 19 195 0.4× 272 1.0× 175 0.7× 269 1.1× 167 0.7× 132 1.1k
Yassine Meraihi Algeria 15 271 0.6× 343 1.3× 183 0.7× 332 1.3× 42 0.2× 48 1.1k
Orhan Yaman Türkiye 19 163 0.4× 227 0.8× 67 0.3× 131 0.5× 141 0.6× 76 1.2k
Jia Uddin Bangladesh 20 346 0.8× 265 1.0× 127 0.5× 143 0.6× 68 0.3× 148 1.4k
Syed Irtaza Haider Saudi Arabia 19 196 0.4× 228 0.9× 59 0.2× 731 2.9× 143 0.6× 41 1.4k
Saud S. Alotaibi Saudi Arabia 21 448 1.0× 353 1.3× 186 0.7× 89 0.4× 111 0.5× 111 1.2k
Akif Durdu Türkiye 20 417 0.9× 358 1.3× 90 0.3× 206 0.8× 286 1.3× 92 1.4k
Jiusi Zhang China 20 160 0.4× 389 1.5× 78 0.3× 335 1.3× 97 0.4× 43 1.9k
Neelu Khare India 13 190 0.4× 533 2.0× 346 1.3× 98 0.4× 295 1.3× 43 1.2k

Countries citing papers authored by Liangzhi Li

Since Specialization
Citations

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

Fields of papers citing papers by Liangzhi Li

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liangzhi Li

This figure shows the co-authorship network connecting the top 25 collaborators of Liangzhi Li. A scholar is included among the top collaborators of Liangzhi Li 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 Liangzhi Li. Liangzhi Li 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.
Chen, Ruijun, et al.. (2025). BlockPruner: Fine-grained Pruning for Large Language Models. 5065–5080. 1 indexed citations
2.
Huang, Ye, Zhen Jiang, Zhiqing Liang, et al.. (2025). Mineral types dominate microbiomes and biogeochemical cycling in acid mine drainage. Water Research. 278. 123367–123367. 1 indexed citations
3.
4.
García, Noa, et al.. (2024). Exploring Emotional Stimuli Detection in Artworks: A Benchmark Dataset and Baselines Evaluation. Journal of Imaging. 10(6). 136–136.
5.
Li, Liangzhi, et al.. (2023). Dual-Feedback Knowledge Retrieval for Task-Oriented Dialogue Systems. 6566–6580. 3 indexed citations
6.
Li, Liangzhi, et al.. (2023). TCRA-LLM: Token Compression Retrieval Augmented Large Language Model for Inference Cost Reduction. 9796–9810. 12 indexed citations
7.
Chen, Guoxin, Yiming Qian, Bowen Wang, & Liangzhi Li. (2023). MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehension. 5163–5175. 2 indexed citations
8.
9.
Wang, Bowen, Liangzhi Li, Yuta Nakashima, Ryo Kawasaki, & Hajime Nagahara. (2023). Real-time estimation of the remaining surgery duration for cataract surgery using deep convolutional neural networks and long short-term memory. BMC Medical Informatics and Decision Making. 23(1). 80–80. 6 indexed citations
10.
Li, Liangzhi, Bowen Wang, Manisha Verma, et al.. (2021). SCOUTER: Slot Attention-based Classifier for Explainable Image Recognition. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). 1026–1035. 31 indexed citations
11.
Wang, Bowen, Liangzhi Li, Manisha Verma, et al.. (2021). MTUNet: Few-shot Image Classification with Visual Explanations. 2294–2298. 23 indexed citations
12.
Wang, Bowen, Liangzhi Li, Yuta Nakashima, et al.. (2021). Noisy-LSTM: Improving Temporal Awareness for Video Semantic Segmentation. IEEE Access. 9. 46810–46820. 17 indexed citations
13.
Wang, Bowen, Liangzhi Li, Yuta Nakashima, et al.. (2021). Image Retrieval by Hierarchy-aware Deep Hashing Based on Multi-task Learning. 486–490. 2 indexed citations
14.
Huang, Ye, Zhen Jiang, Zonglin Liang, et al.. (2021). Key Factors Governing Microbial Community in Extremely Acidic Mine Drainage (pH <3). Frontiers in Microbiology. 12. 761579–761579. 21 indexed citations
15.
Li, Liangzhi, Manisha Verma, Yuta Nakashima, Hajime Nagahara, & Ryo Kawasaki. (2020). IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks. 3645–3654. 218 indexed citations breakdown →
16.
Li, Liangzhi, Kaoru Ota, & Mianxiong Dong. (2018). DeepNFV: A Lightweight Framework for Intelligent Edge Network Functions Virtualization. IEEE Network. 33(1). 136–141. 38 indexed citations
17.
Li, Liangzhi, Kaoru Ota, & Mianxiong Dong. (2018). Human in the Loop: Distributed Deep Model for Mobile Crowdsensing. IEEE Internet of Things Journal. 5(6). 4957–4964. 12 indexed citations
18.
Li, Liangzhi, Kaoru Ota, & Mianxiong Dong. (2018). Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing. IEEE Transactions on Industrial Informatics. 14(10). 4665–4673. 343 indexed citations breakdown →
19.
Chen, Yu, Liangzhi Li, & Zhijun Wu. (2010). Basic Design for cultivating creative thinking. 36. 466–469.
20.

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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