Qingyao Wu

5.3k total citations
116 papers, 3.4k citations indexed

About

Qingyao Wu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Molecular Biology. According to data from OpenAlex, Qingyao Wu has authored 116 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 67 papers in Artificial Intelligence, 61 papers in Computer Vision and Pattern Recognition and 16 papers in Molecular Biology. Recurrent topics in Qingyao Wu's work include Domain Adaptation and Few-Shot Learning (32 papers), Multimodal Machine Learning Applications (24 papers) and Text and Document Classification Technologies (17 papers). Qingyao Wu is often cited by papers focused on Domain Adaptation and Few-Shot Learning (32 papers), Multimodal Machine Learning Applications (24 papers) and Text and Document Classification Technologies (17 papers). Qingyao Wu collaborates with scholars based in China, Hong Kong and Singapore. Qingyao Wu's co-authors include Mingkui Tan, Michael K. Ng, Yunming Ye, Guosheng Lin, Yuguang Yan, Hanrui Wu, Huaqing Min, Junzhou Huang, Joshua Zhexue Huang and Xiaojun Chen and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Scientific Reports and Applied Energy.

In The Last Decade

Qingyao Wu

113 papers receiving 3.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qingyao Wu China 31 1.9k 1.7k 276 224 203 116 3.4k
Zhaohong Deng China 33 2.1k 1.1× 1.2k 0.7× 162 0.6× 206 0.9× 163 0.8× 146 3.6k
Marius Kloft Germany 25 1.7k 0.9× 998 0.6× 147 0.5× 164 0.7× 156 0.8× 86 3.0k
Jiancheng Lv China 29 1.8k 0.9× 2.0k 1.2× 171 0.6× 85 0.4× 166 0.8× 195 3.9k
Wen Li China 29 1.7k 0.9× 2.0k 1.1× 109 0.4× 163 0.7× 122 0.6× 138 3.2k
Dezhong Peng China 28 1.4k 0.7× 2.0k 1.1× 245 0.9× 175 0.8× 139 0.7× 168 3.5k
Zhenmin Tang China 27 872 0.5× 1.3k 0.8× 139 0.5× 190 0.8× 226 1.1× 221 2.6k
Brijesh Verma Australia 29 1.3k 0.7× 1.4k 0.8× 167 0.6× 155 0.7× 125 0.6× 204 2.8k
M. Hassaballah Egypt 27 911 0.5× 1.2k 0.7× 218 0.8× 100 0.4× 111 0.5× 84 2.8k
E. Emary Egypt 25 2.0k 1.0× 653 0.4× 349 1.3× 197 0.9× 188 0.9× 50 3.2k
Yanan Sun China 23 1.8k 0.9× 2.1k 1.2× 216 0.8× 78 0.3× 78 0.4× 91 4.2k

Countries citing papers authored by Qingyao Wu

Since Specialization
Citations

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

Fields of papers citing papers by Qingyao Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qingyao Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Qingyao Wu. A scholar is included among the top collaborators of Qingyao Wu 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 Qingyao Wu. Qingyao Wu 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.
Wu, Qingyao, et al.. (2024). Spatial-Semantic Collaborative Cropping for User Generated Content. Proceedings of the AAAI Conference on Artificial Intelligence. 38(5). 4988–4997. 3 indexed citations
2.
Fang, Chaowei, et al.. (2024). Variance-Insensitive and Target-Preserving Mask Refinement for Interactive Image Segmentation. Proceedings of the AAAI Conference on Artificial Intelligence. 38(2). 1698–1706. 2 indexed citations
3.
Su, Yukun, et al.. (2023). Adaptive Locally-Aligned Transformer for low-light video enhancement. Computer Vision and Image Understanding. 240. 103916–103916.
4.
Zheng, Jiehui, et al.. (2023). Online coal consumption characteristics fitting for daily economic dispatch using a data-driven hybrid sequential model. Applied Energy. 341. 121127–121127. 4 indexed citations
5.
Niu, Shuaicheng, et al.. (2023). Test-Time Model Adaptation for Visual Question Answering With Debiased Self-Supervisions. IEEE Transactions on Multimedia. 26. 2137–2147. 12 indexed citations
6.
Zhang, Chi, et al.. (2021). CycleSegNet: Object Co-Segmentation With Cycle Refinement and Region Correspondence. IEEE Transactions on Image Processing. 30. 5652–5664. 20 indexed citations
7.
Xu, Guanghui, et al.. (2021). Debiased Visual Question Answering from Feature and Sample Perspectives. Neural Information Processing Systems. 34. 26 indexed citations
8.
Yang, Min, Chengming Li, Ying Shen, et al.. (2020). Hierarchical Human-Like Deep Neural Networks for Abstractive Text Summarization. IEEE Transactions on Neural Networks and Learning Systems. 32(6). 2744–2757. 42 indexed citations
9.
Zhang, Yifan, Ying Wei, Qingyao Wu, et al.. (2020). Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis. IEEE Transactions on Image Processing. 29. 7834–7844. 113 indexed citations
10.
Deng, Chaorui, Qi Wu, Qingyao Wu, et al.. (2020). Visual Grounding Via Accumulated Attention. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(3). 1670–1684. 10 indexed citations
11.
Chen, Qi, Qi Wu, Jian Chen, et al.. (2020). Scripted Video Generation With a Bottom-Up Generative Adversarial Network. IEEE Transactions on Image Processing. 29. 7454–7467. 20 indexed citations
12.
Cao, Jiezhang, Yong Guo, Qingyao Wu, et al.. (2020). Improving Generative Adversarial Networks With Local Coordinate Coding. IEEE Transactions on Pattern Analysis and Machine Intelligence. 44(1). 211–227. 11 indexed citations
13.
Han, Chao, Jian Chen, Mingkui Tan, Michael K. Ng, & Qingyao Wu. (2020). A Tensor-Based Markov Chain Model for Heterogeneous Information Network Collective Classification. IEEE Transactions on Knowledge and Data Engineering. 34(9). 4063–4076. 1 indexed citations
14.
Lyu, Fan, Qi Wu, Fuyuan Hu, Qingyao Wu, & Mingkui Tan. (2019). Attend and Imagine: Multi-Label Image Classification With Visual Attention and Recurrent Neural Networks. IEEE Transactions on Multimedia. 21(8). 1971–1981. 56 indexed citations
15.
Zhang, Yifan, Peilin Zhao, Shuaicheng Niu, et al.. (2019). Online Adaptive Asymmetric Active Learning With Limited Budgets. IEEE Transactions on Knowledge and Data Engineering. 33(6). 2680–2692. 21 indexed citations
16.
Guo, Yong, Qi Chen, Jian Chen, et al.. (2019). Auto-Embedding Generative Adversarial Networks For High Resolution Image Synthesis. IEEE Transactions on Multimedia. 21(11). 2726–2737. 56 indexed citations
17.
Zeng, Runhao, Chuang Gan, Peihao Chen, et al.. (2019). Breaking Winner-Takes-All: Iterative-Winners-Out Networks for Weakly Supervised Temporal Action Localization. IEEE Transactions on Image Processing. 28(12). 5797–5808. 74 indexed citations
18.
Cao, Jiezhang, Yong Guo, Qingyao Wu, et al.. (2018). Adversarial Learning with Local Coordinate Coding. International Conference on Machine Learning. 707–715. 7 indexed citations
19.
Zhuang, Zhuangwei, Mingkui Tan, Bohan Zhuang, et al.. (2018). Discrimination-aware channel pruning for deep neural networks. neural information processing systems. 31. 883–894. 209 indexed citations
20.
Tan, Mingkui, et al.. (2018). Cartoon-to-Photo Facial Translation with Generative Adversarial Networks. Asian Conference on Machine Learning. 566–581. 3 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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