Liejun Wang

3.2k total citations · 2 hit papers
172 papers, 2.0k citations indexed

About

Liejun Wang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Liejun Wang has authored 172 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 108 papers in Computer Vision and Pattern Recognition, 48 papers in Artificial Intelligence and 39 papers in Media Technology. Recurrent topics in Liejun Wang's work include Advanced Image and Video Retrieval Techniques (32 papers), Advanced Neural Network Applications (27 papers) and Remote-Sensing Image Classification (23 papers). Liejun Wang is often cited by papers focused on Advanced Image and Video Retrieval Techniques (32 papers), Advanced Neural Network Applications (27 papers) and Remote-Sensing Image Classification (23 papers). Liejun Wang collaborates with scholars based in China, New Zealand and Macao. Liejun Wang's co-authors include Shuli Cheng, Yongming Li, Cui Zhang, Shuli Cheng, Fan Li, Haojin Li, Lijun He, Shiji Song, Xinyu Huang and Danny Chen and has published in prestigious journals such as PLoS ONE, Scientific Reports and IEEE Transactions on Geoscience and Remote Sensing.

In The Last Decade

Liejun Wang

153 papers receiving 1.9k citations

Hit Papers

SwinSUNet: Pure Transformer Network for Remote Sensing Im... 2022 2026 2023 2024 2022 2023 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. SwinSUNet: Pure Transformer Network for Remote Sensing Image Change Detection
    2022 311 citations Liejun Wang, Shuli Cheng et al. profile →
  2. Asymmetric Cross-Attention Hierarchical Network Based on CNN and Transformer for Bitemporal Remote Sensing Images Change Detection
    2023 96 citations Shuli Cheng, Liejun Wang 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
Liejun Wang China 22 843 542 443 269 221 172 2.0k
Jakub Nalepa Poland 24 849 1.0× 565 1.0× 664 1.5× 204 0.8× 78 0.4× 120 2.2k
Chih‐Cheng Hung United States 19 592 0.7× 374 0.7× 440 1.0× 189 0.7× 76 0.3× 130 1.7k
Michał Kawulok Poland 21 924 1.1× 405 0.7× 465 1.0× 116 0.4× 43 0.2× 91 1.9k
Yuhui Zheng China 27 1.6k 1.9× 931 1.7× 435 1.0× 331 1.2× 104 0.5× 96 2.6k
Ming Wu China 16 911 1.1× 367 0.7× 460 1.0× 77 0.3× 233 1.1× 75 1.8k
Yuhu Cheng China 24 577 0.7× 476 0.9× 725 1.6× 175 0.7× 120 0.5× 169 2.0k
V. Sowmya India 21 461 0.5× 299 0.6× 376 0.8× 97 0.4× 169 0.8× 143 1.6k
Jingjing Ma China 23 788 0.9× 621 1.1× 570 1.3× 203 0.8× 120 0.5× 65 1.8k

Countries citing papers authored by Liejun Wang

Since Specialization
Citations

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

Fields of papers citing papers by Liejun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Liejun Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Liejun Wang. A scholar is included among the top collaborators of Liejun Wang 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 Liejun Wang. Liejun Wang 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.
Cheng, Shuli, et al.. (2025). Embedded Separate Deep Localization Feature Information Vision Transformer for Hash Image Retrieval. Expert Systems with Applications. 274. 126902–126902. 4 indexed citations
2.
3.
Zhang, L., Shuli Cheng, & Liejun Wang. (2025). Label-guided diversified learning model for occluded person re-identification. Expert Systems with Applications. 272. 126745–126745. 1 indexed citations
4.
Wang, Liejun, et al.. (2025). Dynamic scheduling for multi-objective flexible job shop via deep reinforcement learning. Applied Soft Computing. 171. 112787–112787. 6 indexed citations
5.
Liu, Shiwei, et al.. (2025). Conv-SdMLPMixer: A hybrid medical image classification network based on multi-branch CNN and multi-scale multi-dimensional MLP. Information Fusion. 118. 102937–102937. 4 indexed citations
6.
Guo, Zhiqing, et al.. (2025). WaveGuard: Robust Deepfake Detection and Source Tracing via Dual-Tree Complex Wavelet and Graph Neural Networks. IEEE Transactions on Circuits and Systems for Video Technology. 1–1.
7.
Wang, Liejun, et al.. (2025). MLK-Net: Leveraging multi-scale and large kernel convolutions for robust skin lesion segmentation. Expert Systems with Applications. 276. 127135–127135. 2 indexed citations
8.
Cheng, Shuli, et al.. (2024). Medical image segmentation based on dynamic positioning and region-aware attention. Pattern Recognition. 151. 110375–110375. 8 indexed citations
9.
Wang, Liejun, et al.. (2024). DRA-Net: Medical image segmentation based on adaptive feature extraction and region-level information fusion. Scientific Reports. 14(1). 9714–9714. 8 indexed citations
10.
Zheng, Panpan, et al.. (2024). FCDHNet: A feature cross-dimensional hybrid network for RGB-D salient object detection. Expert Systems with Applications. 266. 126019–126019. 2 indexed citations
11.
Wang, Liejun, et al.. (2024). Asymmetric deep interaction network for RGB-D salient object detection. Expert Systems with Applications. 266. 126083–126083. 2 indexed citations
12.
Guo, Zhiqing, et al.. (2024). TBC-MI : Suppressing noise labels by maximizing cleaning samples for robust image classification. Information Processing & Management. 61(5). 103801–103801. 5 indexed citations
13.
Guo, Zhiqing, et al.. (2024). Medical image segmentation network based on feature filtering with low number of parameters. Applied Soft Computing. 167. 112399–112399.
14.
Cheng, Shuli, et al.. (2023). Combined query image retrieval based on hybrid coding of CNN and Mix-Transformer. Expert Systems with Applications. 234. 121060–121060. 6 indexed citations
15.
Wang, Liejun, et al.. (2023). Dynamic hierarchical multi-scale fusion network with axial MLP for medical image segmentation. Scientific Reports. 13(1). 6342–6342. 7 indexed citations
16.
Wang, Liejun, et al.. (2023). Solving flexible job shop scheduling problems via deep reinforcement learning. Expert Systems with Applications. 245. 123019–123019. 55 indexed citations
17.
Cheng, Shuli, et al.. (2023). Solving job shop scheduling problems via deep reinforcement learning. Applied Soft Computing. 143. 110436–110436. 37 indexed citations
18.
Xiao, Xingming, et al.. (2023). Deep hashing image retrieval based on hybrid neural network and optimized metric learning. Knowledge-Based Systems. 284. 111336–111336. 15 indexed citations
19.
Cheng, Shuli, et al.. (2022). Efficient Image Super-Resolution via Self-Calibrated Feature Fuse. Sensors. 22(1). 329–329. 7 indexed citations
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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Jakub Nalepa Breakdown of academic impact, for papers by Chih‐Cheng Hung Breakdown of academic impact, for papers by Michał Kawulok Breakdown of academic impact, for papers by Yuhui Zheng Breakdown of academic impact, for papers by Ming Wu Breakdown of academic impact, for papers by Yuhu Cheng Breakdown of academic impact, for papers by V. Sowmya Breakdown of academic impact, for papers by Jingjing Ma
Rankless by CCL
2026