Quan Wang

6.2k total citations · 1 hit paper
202 papers, 3.6k citations indexed

About

Quan Wang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Networks and Communications. According to data from OpenAlex, Quan Wang has authored 202 papers receiving a total of 3.6k indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Computer Vision and Pattern Recognition, 61 papers in Artificial Intelligence and 52 papers in Computer Networks and Communications. Recurrent topics in Quan Wang's work include Advanced Image and Video Retrieval Techniques (22 papers), Topic Modeling (15 papers) and IoT and Edge/Fog Computing (15 papers). Quan Wang is often cited by papers focused on Advanced Image and Video Retrieval Techniques (22 papers), Topic Modeling (15 papers) and IoT and Edge/Fog Computing (15 papers). Quan Wang collaborates with scholars based in China, United States and United Kingdom. Quan Wang's co-authors include Li Wan, Deyu Lin, Ignacio López Moreno, Di Wang, Xinbo Gao, Jin Jiang, Pengfei Yang, Zhiqiang Zhang, Zhendong Mao and Yajuan Lyu and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and Journal of Controlled Release.

In The Last Decade

Quan Wang

174 papers receiving 3.4k citations

Hit Papers

Generalized End-to-End Loss for Speaker Verification 2018 2026 2020 2023 2018 100 200 300 400
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. Generalized End-to-End Loss for Speaker Verification
    2018 455 citations Quan 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
Quan Wang China 28 1.7k 919 848 701 460 202 3.6k
Ke Yang China 14 1.9k 1.2× 1.2k 1.3× 863 1.0× 661 0.9× 264 0.6× 62 3.4k
Rajat Monga United States 6 2.3k 1.4× 2.2k 2.4× 414 0.5× 486 0.7× 318 0.7× 8 4.0k
Tom Schaul United States 26 3.3k 2.0× 877 1.0× 634 0.7× 229 0.3× 634 1.4× 54 4.9k
Huanlai Xing China 29 1.2k 0.7× 536 0.6× 1.4k 1.6× 265 0.4× 658 1.4× 145 3.2k
Ahmed Ghoneim Saudi Arabia 32 1.0k 0.6× 670 0.7× 1.2k 1.4× 189 0.3× 503 1.1× 78 2.9k
Jiadi Yu China 31 920 0.6× 634 0.7× 713 0.8× 695 1.0× 771 1.7× 186 3.2k
Pradeep K. Atrey United States 23 962 0.6× 1.2k 1.3× 349 0.4× 660 0.9× 146 0.3× 126 2.7k
Shimon Whiteson Netherlands 27 2.3k 1.4× 466 0.5× 612 0.7× 130 0.2× 310 0.7× 136 3.5k
Prabir Bhattacharya United States 26 777 0.5× 791 0.9× 397 0.5× 392 0.6× 308 0.7× 179 2.8k
Musheer Ahmad India 40 1.9k 1.2× 2.7k 3.0× 611 0.7× 182 0.3× 379 0.8× 152 4.1k

Countries citing papers authored by Quan Wang

Since Specialization
Citations

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

Fields of papers citing papers by Quan Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Quan Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Quan Wang. A scholar is included among the top collaborators of Quan 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 Quan Wang. Quan 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.
2.
Xiao, Liang, Di Wang, Bin Jing, et al.. (2025). Fine-grained knowledge fusion for retrieval-augmented medical visual question answering. Information Fusion. 120. 103059–103059.
3.
Xiao, Liang, et al.. (2024). Candidate-Heuristic In-Context Learning: A new framework for enhancing medical visual question answering with LLMs. Information Processing & Management. 61(5). 103805–103805. 14 indexed citations
4.
Wang, Di, et al.. (2024). NIV-SSD: Neighbor IoU-voting single-stage object detector from point cloud. Neurocomputing. 597. 127987–127987.
5.
Wang, Di, et al.. (2024). Gist, Content, Target-Oriented: A 3-Level Human-Like Framework for Video Moment Retrieval. IEEE Transactions on Multimedia. 26. 11044–11056. 2 indexed citations
6.
Chen, Huiling, Xi Qiao, Bo Liu, et al.. (2024). IPMCNet: A Lightweight Algorithm for Invasive Plant Multiclassification. Agronomy. 14(2). 333–333. 2 indexed citations
7.
Yang, Pengfei, et al.. (2024). Fine-grained complexity-driven latency predictor in hardware-aware neural architecture search using composite loss. Information Sciences. 676. 120783–120783.
8.
Wang, Yan, et al.. (2024). Ada-FA: A Comprehensive Framework for Adaptive Fault Tolerance and Aging Mitigation in FPGAs. IEEE Internet of Things Journal. 11(10). 17688–17699. 1 indexed citations
9.
Wang, Haiyue, Quan Wang, Qiguang Miao, & Xiaoke Ma. (2023). Joint learning of data recovering and graph contrastive denoising for incomplete multi-view clustering. Information Fusion. 104. 102155–102155. 20 indexed citations
10.
Wang, Quan, et al.. (2023). Text Style Transfer with Contrastive Transfer Pattern Mining. 7914–7927.
11.
Yang, Pengfei, et al.. (2023). Efficient and accurate compound scaling for convolutional neural networks. Neural Networks. 167. 787–797. 18 indexed citations
12.
Yang, Pengfei, et al.. (2023). SLAPP: Subgraph-level attention-based performance prediction for deep learning models. Neural Networks. 170. 285–297. 2 indexed citations
13.
Xiao, Liang, Di Wang, Quan Wang, et al.. (2023). Language-Guided Visual Aggregation Network for Video Question Answering. 5195–5203. 2 indexed citations
14.
15.
Yang, Pengfei, et al.. (2022). Energy Consumption and QoS-Aware Co-Offloading for Vehicular Edge Computing. IEEE Internet of Things Journal. 10(6). 5214–5225. 20 indexed citations
16.
An, Lingling, et al.. (2022). Flexible Learning Models Utilizing Different Neural Plasticities. IEEE Transactions on Cognitive and Developmental Systems. 15(3). 1150–1160. 1 indexed citations
17.
Wang, Quan, et al.. (2021). RPPUF: An Ultra-Lightweight Reconfigurable Pico-Physically Unclonable Function for Resource-Constrained IoT Devices. Electronics. 10(23). 3039–3039. 4 indexed citations
18.
Peng, Weihua, Yuguang Chen, Quan Wang, et al.. (2020). Event Extraction as Multi-turn Question Answering. 829–838. 95 indexed citations
19.
An, Yang, Quan Wang, Jing Liu, et al.. (2019). Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension. 2346–2357. 80 indexed citations
20.
Wang, Quan, et al.. (2019). A PUF-based unified identity verification framework for secure IoT hardware via device authentication. World Wide Web. 23(2). 1057–1088. 37 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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