Chenwei Tang

576 total citations
30 papers, 252 citations indexed

About

Chenwei Tang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Chenwei Tang has authored 30 papers receiving a total of 252 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Computer Vision and Pattern Recognition, 13 papers in Artificial Intelligence and 5 papers in Cognitive Neuroscience. Recurrent topics in Chenwei Tang's work include Generative Adversarial Networks and Image Synthesis (6 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Advanced Neural Network Applications (4 papers). Chenwei Tang is often cited by papers focused on Generative Adversarial Networks and Image Synthesis (6 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Advanced Neural Network Applications (4 papers). Chenwei Tang collaborates with scholars based in China and United States. Chenwei Tang's co-authors include Jiancheng Lv, Zhenan He, Shudong Huang, Hongjie Wu, Yunxia Li, Caiyang Yu, Jian Wang, Yancheng Zhang, Yongsheng Sang and Wentao Feng and has published in prestigious journals such as Pattern Recognition, Information Sciences and IEEE Transactions on Industrial Informatics.

In The Last Decade

Chenwei Tang

26 papers receiving 243 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chenwei Tang China	10	109	86	36	29	19	30	252
Alessio Tonioni Italy	11	284 2.6×	84 1.0×	21 0.6×	24 0.8×	40 2.1×	18	391
P. Paalanen Finland	5	160 1.5×	62 0.7×	27 0.8×	21 0.7×	22 1.2×	7	292
ShahRukh Athar United States	8	239 2.2×	118 1.4×	25 0.7×	30 1.0×	19 1.0×	17	413
Fereshteh Sadeghi Iran	9	188 1.7×	121 1.4×	9 0.3×	47 1.6×	25 1.3×	19	285
Abd El Rahman Shabayek Luxembourg	9	155 1.4×	61 0.7×	13 0.4×	19 0.7×	68 3.6×	32	305
Mengyang Pu China	7	198 1.8×	71 0.8×	11 0.3×	5 0.2×	24 1.3×	13	287
Ruoxi Deng China	6	138 1.3×	73 0.8×	20 0.6×	9 0.3×	7 0.4×	12	237
Buyu Liu United States	10	185 1.7×	67 0.8×	18 0.5×	11 0.4×	39 2.1×	18	244
Evgeny A. Semenishchev Russia	9	181 1.7×	19 0.2×	15 0.4×	16 0.6×	32 1.7×	105	307
Aouaidjia Kamel China	6	247 2.3×	86 1.0×	13 0.4×	24 0.8×	12 0.6×	10	333

Countries citing papers authored by Chenwei Tang

Since Specialization

Citations

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

Fields of papers citing papers by Chenwei Tang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chenwei Tang

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

Xiao, Rong, et al.. (2025). STSF: Spiking Time Sparse Feedback Learning for Spiking Neural Networks. IEEE Transactions on Neural Networks and Learning Systems. 36(6). 11479–11492.

Huang, Shudong, Wentao Feng, Chenwei Tang, et al.. (2025). Partial Differential Equations Meet Deep Neural Networks: A Survey. IEEE Transactions on Neural Networks and Learning Systems. 36(8). 13649–13669. 7 indexed citations

Li, Mao, et al.. (2025). GDM-BC: Non-invasive body composition dataset for intelligent prediction of Gestational Diabetes Mellitus. Computers in Biology and Medicine. 192(Pt A). 110176–110176. 1 indexed citations

Feng, Wentao, et al.. (2025). DSAIS-PINN: Dynamic seeds allocation importance sampling for physics-informed neural networks. Neurocomputing. 648. 130578–130578.

Wu, Hongjie, et al.. (2024). Self-supervised Domain Adaptation with Significance-Oriented Masking for Pelvic Organ Prolapse detection. Pattern Recognition Letters. 185. 94–100.

Han, Bo, Lihuo He, Junjie Ke, Chenwei Tang, & Xinbo Gao. (2024). Weighted parallel decoupled feature pyramid network for object detection. Neurocomputing. 593. 127809–127809. 6 indexed citations

Xiao, Rong, et al.. (2024). Multi-attribute dynamic attenuation learning improved spiking actor network. Neurocomputing. 614. 128819–128819. 1 indexed citations

Feng, Wentao, Xiao Rong, Lihuo He, et al.. (2024). Improving generalized zero-shot learning via cluster-based semantic disentangling representation. Pattern Recognition. 150. 110320–110320. 4 indexed citations

Wang, Shuyang, Tao Yang, Zhenghao Li, et al.. (2024). LSD-YOLO: Enhanced YOLOv8n Algorithm for Efficient Detection of Lemon Surface Diseases. Plants. 13(15). 2069–2069. 6 indexed citations

10.

Tang, Chenwei, Jialiang Huang, Fan Yang, et al.. (2024). Attention-based early warning framework for abnormal operating conditions in fluid catalytic cracking units. Applied Soft Computing. 153. 111275–111275. 2 indexed citations

11.

Yu, Caiyang, et al.. (2024). GPT-NAS: Neural Architecture Search Meets Generative Pre-Trained Transformer Model. Big Data Mining and Analytics. 8(1). 45–64. 1 indexed citations

12.

Lin, Chen‐Yu, et al.. (2024). Zero-Shot Aerial Object Detection with Visual Description Regularization. Proceedings of the AAAI Conference on Artificial Intelligence. 38(7). 6926–6934. 5 indexed citations

13.

Huang, Shudong, et al.. (2023). Smooth representation learning from multi-view data. Information Fusion. 100. 101916–101916. 9 indexed citations

14.

Liu, Xinyu, et al.. (2023). MPQ-YOLO: Ultra low mixed-precision quantization of YOLO for edge devices deployment. Neurocomputing. 574. 127210–127210. 19 indexed citations

15.

Yu, Caiyang, Yixi Wang, Chenwei Tang, Wentao Feng, & Jiancheng Lv. (2023). EU-Net: Automatic U-Net neural architecture search with differential evolutionary algorithm for medical image segmentation. Computers in Biology and Medicine. 167. 107579–107579. 16 indexed citations

16.

Wang, Guangjin, et al.. (2022). Open-Set Classification for Signal Diagnosis of Machinery Sensor in Industrial Environment. IEEE Transactions on Industrial Informatics. 19(3). 2574–2584. 20 indexed citations

17.

Tang, Chenwei, Zhenan He, Yunxia Li, & Jiancheng Lv. (2021). Zero-Shot Learning via Structure-Aligned Generative Adversarial Network. IEEE Transactions on Neural Networks and Learning Systems. 33(11). 6749–6762. 26 indexed citations

18.

Mao, Li, et al.. (2021). Combination of certainty and uncertainty: Using FusionGAN to create abstract paintings. Neural Networks. 144. 443–454. 3 indexed citations

19.

Tang, Chenwei, et al.. (2020). Arbitrary Chinese Font Generation from a Single Reference. 1–7. 3 indexed citations

20.

Mao, Li, Jiancheng Lv, & Chenwei Tang. (2019). Aesthetic assessment of paintings based on visual balance. IET Image Processing. 13(14). 2821–2828. 9 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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