Qi Wu

11.1k total citations · 2 hit papers
160 papers, 5.5k citations indexed

About

Qi Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Qi Wu has authored 160 papers receiving a total of 5.5k indexed citations (citations by other indexed papers that have themselves been cited), including 120 papers in Computer Vision and Pattern Recognition, 69 papers in Artificial Intelligence and 16 papers in Signal Processing. Recurrent topics in Qi Wu's work include Multimodal Machine Learning Applications (82 papers), Advanced Image and Video Retrieval Techniques (68 papers) and Domain Adaptation and Few-Shot Learning (40 papers). Qi Wu is often cited by papers focused on Multimodal Machine Learning Applications (82 papers), Advanced Image and Video Retrieval Techniques (68 papers) and Domain Adaptation and Few-Shot Learning (40 papers). Qi Wu collaborates with scholars based in China, Australia and United States. Qi Wu's co-authors include Chunhua Shen, Anton van den Hengel, Peng Wang, Anthony Dick, Shizhe Chen, Yuankai Qi, Qin Jin, Yicong Hong, Yutong Xie and Yong Xia 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

Qi Wu

149 papers receiving 5.4k citations

Hit Papers

What Value Do Explicit High Level Concepts Have in Vision... 2016 2026 2019 2022 2016 2019 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. What Value Do Explicit High Level Concepts Have in Vision to Language Problems?
    2016 315 citations Qi Wu et al. Adelaide Research & Scholarship (AR&S) (University of Adelaide) profile →
  2. Medical image classification using synergic deep learning
    2019 269 citations Jianpeng Zhang, Yutong Xie et al. Medical Image Analysis profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Qi Wu China 38 4.3k 2.9k 266 229 177 160 5.5k
Kelvin Xu United States 6 3.0k 0.7× 2.3k 0.8× 144 0.5× 223 1.0× 151 0.9× 10 4.5k
Ryan Kiros Canada 8 3.6k 0.8× 3.0k 1.0× 151 0.6× 250 1.1× 155 0.9× 11 5.3k
Yuanjun Xiong Hong Kong 18 5.1k 1.2× 3.2k 1.1× 123 0.5× 269 1.2× 204 1.2× 34 6.7k
Sanketh Shetty France 4 3.7k 0.9× 2.0k 0.7× 152 0.6× 268 1.2× 123 0.7× 6 4.8k
Jun Xiao China 31 3.8k 0.9× 2.3k 0.8× 102 0.4× 154 0.7× 226 1.3× 175 5.2k
Shenghua Gao China 41 4.4k 1.0× 3.4k 1.2× 146 0.5× 426 1.9× 529 3.0× 111 6.7k
Mu Li China 23 1.6k 0.4× 2.0k 0.7× 323 1.2× 190 0.8× 228 1.3× 61 4.0k
Xiaopeng Hong China 35 3.3k 0.8× 1.7k 0.6× 223 0.8× 494 2.2× 172 1.0× 146 4.8k
Zhaoxiang Zhang China 39 5.1k 1.2× 1.9k 0.7× 291 1.1× 310 1.4× 428 2.4× 230 6.5k
Xiaodan Liang China 51 6.6k 1.5× 3.7k 1.3× 166 0.6× 232 1.0× 455 2.6× 239 8.7k

Countries citing papers authored by Qi Wu

Since Specialization
Citations

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

Fields of papers citing papers by Qi Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Qi Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Qi Wu. A scholar is included among the top collaborators of Qi 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 Qi Wu. Qi 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
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Xie, Yutong, Lin Gu, Tatsuya Harada, et al.. (2024). Rethinking masked image modelling for medical image representation. Medical Image Analysis. 98. 103304–103304. 7 indexed citations
3.
Xie, Yutong, Jianpeng Zhang, Yong Xia, & Qi Wu. (2024). UniMiSS+: Universal Medical Self-Supervised Learning From Cross-Dimensional Unpaired Data. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(12). 10021–10035. 3 indexed citations
4.
Wang, Sen, et al.. (2024). Navigating Beyond Instructions: Vision-and-Language Navigation in Obstructed Environments. 7639–7648. 1 indexed citations
5.
6.
Deng, Chaorui, et al.. (2023). Image Captioning With Controllable and Adaptive Length Levels. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(2). 764–779. 5 indexed citations
7.
Tan, Mingkui, et al.. (2023). Transformer-Based Relational Inference Network for Complex Visual Relational Reasoning. ACM Transactions on Multimedia Computing Communications and Applications. 20(1). 1–23. 6 indexed citations
8.
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Qi, Yuankai, et al.. (2023). HOP+: History-Enhanced and Order-Aware Pre-Training for Vision-and-Language Navigation. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(7). 1–14. 35 indexed citations
10.
Gao, Chen, et al.. (2023). Room-Object Entity Prompting and Reasoning for Embodied Referring Expression. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(2). 994–1010. 11 indexed citations
11.
Wu, Qi, Peng Wang, Xin Wang, Xiaodong He, & Wenwu Zhu. (2022). Visual Question Answering. 3 indexed citations
12.
Sun, Mengyang, et al.. (2022). Rethinking and Improving Feature Pyramids for One-Stage Referring Expression Comprehension. IEEE Transactions on Image Processing. 32. 854–864. 11 indexed citations
13.
Ling, Liuyi, et al.. (2022). A Lightweight Bearing Fault Diagnosis Method Based on Multi-Channel Depthwise Separable Convolutional Neural Network. Electronics. 11(24). 4110–4110. 11 indexed citations
14.
Sun, Mengyang, et al.. (2022). A Proposal-Free One-Stage Framework for Referring Expression Comprehension and Generation via Dense Cross-Attention. IEEE Transactions on Multimedia. 25. 2446–2458. 34 indexed citations
15.
Zhang, Lili, et al.. (2021). A Study on Seizure Detection of EEG Signals Represented in 2D. Sensors. 21(15). 5145–5145. 12 indexed citations
16.
Wu, Qi, et al.. (2021). Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 10 indexed citations
17.
Chen, Shizhe, Yida Zhao, Qin Jin, & Qi Wu. (2020). Fine-Grained Video-Text Retrieval With Hierarchical Graph Reasoning. 10635–10644. 213 indexed citations
18.
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
19.
Huang, Yan, Qi Wu, Wei Wang, & Liang Wang. (2018). Image and Sentence Matching via Semantic Concepts and Order Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence. 42(3). 636–650. 31 indexed citations
20.
Wang, Cong, Rui Zhao, Xiao Yang, & Qi Wu. (2018). Research of UAV target detection and flight control based on deep learning. 170–174. 17 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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