Peng Tang

2.7k total citations
31 papers, 1.5k citations indexed

About

Peng Tang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Civil and Structural Engineering. According to data from OpenAlex, Peng Tang has authored 31 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Computer Vision and Pattern Recognition, 13 papers in Artificial Intelligence and 3 papers in Civil and Structural Engineering. Recurrent topics in Peng Tang's work include Advanced Image and Video Retrieval Techniques (13 papers), Advanced Neural Network Applications (11 papers) and Image Retrieval and Classification Techniques (6 papers). Peng Tang is often cited by papers focused on Advanced Image and Video Retrieval Techniques (13 papers), Advanced Neural Network Applications (11 papers) and Image Retrieval and Classification Techniques (6 papers). Peng Tang collaborates with scholars based in China, United States and United Kingdom. Peng Tang's co-authors include Xinggang Wang, Wenyu Liu, Xiang Bai, Song Bai, Xiang Bai, Alan Yuille, Wei Shen, Longin Jan Latecki, Philip H. S. Torr and Yuyin Zhou and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and IEEE Access.

In The Last Decade

Peng Tang

31 papers receiving 1.5k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Peng Tang China	14	1.2k	619	232	169	91	31	1.5k
Henghui Ding Singapore	29	1.8k 1.5×	922 1.5×	147 0.6×	127 0.8×	100 1.1×	78	2.3k
Xinlei Chen China	11	639 0.5×	377 0.6×	89 0.4×	152 0.9×	53 0.6×	24	1.2k
Zhe Zhu China	15	922 0.8×	349 0.6×	340 1.5×	303 1.8×	87 1.0×	38	1.5k
Qi Dai China	19	1.2k 1.0×	644 1.0×	141 0.6×	90 0.5×	82 0.9×	39	1.6k
Xianxu Hou China	16	646 0.5×	297 0.5×	108 0.5×	102 0.6×	62 0.7×	38	959
Zhenbing Liu China	24	961 0.8×	688 1.1×	413 1.8×	300 1.8×	59 0.6×	101	1.8k
Yichen Zhou China	9	512 0.4×	271 0.4×	138 0.6×	79 0.5×	75 0.8×	31	1.0k
Dashan Gao United States	20	1.4k 1.2×	340 0.5×	159 0.7×	323 1.9×	85 0.9×	43	1.9k
Suha Kwak South Korea	20	1.8k 1.5×	971 1.6×	172 0.7×	146 0.9×	132 1.5×	53	2.3k
Ezzeddine Zagrouba Tunisia	17	989 0.8×	487 0.8×	343 1.5×	190 1.1×	63 0.7×	132	1.5k

Countries citing papers authored by Peng Tang

Since Specialization

Citations

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

Fields of papers citing papers by Peng Tang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Peng Tang

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

Tang, Peng, et al.. (2024). Fine-Grained Contrastive Learning for Pulmonary Nodule Classification. 56. 1–8. 1 indexed citations

Yang, Nan, Fengyi Li, Peng Tang, et al.. (2022). Automatic fine-grained glomerular lesion recognition in kidney pathology. Pattern Recognition. 127. 108648–108648. 7 indexed citations

Wang, Yan, Peng Tang, Yuyin Zhou, et al.. (2021). Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction. IEEE Transactions on Medical Imaging. 40(10). 2723–2735. 23 indexed citations

Li, Dongming, Peng Tang, Run Zhang, et al.. (2021). Robust Blood Cell Image Segmentation Method Based on Neural Ordinary Differential Equations. Computational and Mathematical Methods in Medicine. 2021. 1–11. 11 indexed citations

Tang, Peng, Chetan Ramaiah, Yan Wang, Ran Xu, & Caiming Xiong. (2021). Proposal Learning for Semi-Supervised Object Detection. 2290–2300. 68 indexed citations

Tang, Peng, et al.. (2019). Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models.. arXiv (Cornell University). 3213–3219. 5 indexed citations

Wang, Xinggang, et al.. (2019). Bag similarity network for deep multi-instance learning. Information Sciences. 504. 578–588. 17 indexed citations

Bai, Song, Peng Tang, Philip H. S. Torr, & Longin Jan Latecki. (2019). Re-Ranking via Metric Fusion for Object Retrieval and Person Re-Identification. 740–749. 75 indexed citations

Li, Yan, et al.. (2019). Optimized Password Recovery based on GPUs for SM3 Algorithm. 1–5. 1 indexed citations

10.

Tang, Peng, Xinggang Wang, Song Bai, et al.. (2018). PCL: Proposal Cluster Learning for Weakly Supervised Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 42(1). 176–191. 289 indexed citations

11.

Yang, Pei, Weidong Jin, & Peng Tang. (2018). Anomaly Detection of Railway Catenary Based on Deep Convolutional Generative Adversarial Networks. 1366–1370. 4 indexed citations

12.

Tang, Peng, Xinggang Wang, Xiang Bai, & Wenyu Liu. (2017). Multiple Instance Detection Network with Online Instance Classifier Refinement. 3059–3067. 308 indexed citations

13.

Wang, Dan, et al.. (2017). Adaptive Solitary Pulmonary Nodule Segmentation for Digital Radiography Images Based on Random Walks and Sequential Filter. IEEE Access. 5. 1460–1468. 7 indexed citations

14.

Tang, Peng, Jin Zhang, Xinggang Wang, et al.. (2016). Learning extremely shared middle-level image representation for scene classification. Knowledge and Information Systems. 52(2). 509–530. 7 indexed citations

15.

Tang, Peng, et al.. (2014). Visual abnormality detection framework for train-mounted pantograph headline surveillance. 78. 847–852. 7 indexed citations

16.

Tang, Peng. (2009). Review on Derivative-Free Nonlinear Bayesian Filtering Methods. Journal of Astronautics. 1 indexed citations

17.

Tang, Peng, et al.. (2008). Video object segmentation based on graph cut with dynamic shape prior constraint. Proceedings - International Conference on Pattern Recognition. 1–4. 5 indexed citations

18.

Tang, Peng & Jing Xiao. (2008). Automatic Generation of High-level Contact State Space between 3D Curved Objects. The International Journal of Robotics Research. 27(7). 832–854. 14 indexed citations

19.

Tang, Peng, et al.. (2007). Real time object tracking using adaptive Kalman particle filter. Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 6786. 67863O–67863O. 2 indexed citations

20.

Tang, Peng & Jing Xiao. (2006). Automatic generation of contact state graphs based on curvature monotonic segmentation. 2633–2640. 5 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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