Chongruo Wu

2.6k total citations · 1 hit paper
11 papers, 866 citations indexed

About

Chongruo Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Chongruo Wu has authored 11 papers receiving a total of 866 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 3 papers in Cognitive Neuroscience. Recurrent topics in Chongruo Wu's work include Advanced Neural Network Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Multimodal Machine Learning Applications (3 papers). Chongruo Wu is often cited by papers focused on Advanced Neural Network Applications (4 papers), Domain Adaptation and Few-Shot Learning (4 papers) and Multimodal Machine Learning Applications (3 papers). Chongruo Wu collaborates with scholars based in United States, China and Hong Kong. Chongruo Wu's co-authors include Mu Li, Yi Zhu, Hang Zhang, Tong He, R. Manmatha, Alexander J. Smola, Zhongyue Zhang, Jonas Mueller, Haibin Lin and Yue Sun and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Neurocomputing and Signal Processing Image Communication.

In The Last Decade

Chongruo Wu

10 papers receiving 855 citations

Hit Papers

align trajectories

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.

ResNeSt: Split-Attention Networks

2022 629 citations Hang Zhang, Chongruo Wu et al. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) profile →

Peers

Chongruo Wu

CVPR

AI

MT

RNMI

CN

Bo Dong China

Theodoros Georgiou Cyprus

Xin Tan China

Diganta Misra India

Lingxiao Yang China

Snehasis Mukherjee India

Xinlei Chen China

Zhongchao Shi China

Florian Schroff United States

Bo Dong China

Chongruo Wu

385 ×0.9

CVPR

155 ×0.6

AI

86 ×0.7

MT

114 ×1.2

RNMI

56 ×0.7

CN

Citations per year, relative to Chongruo Wu Chongruo Wu (= 1×) peers Bo Dong

Countries citing papers authored by Chongruo Wu

Since Specialization

Citations

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

Fields of papers citing papers by Chongruo Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chongruo Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Chongruo Wu. A scholar is included among the top collaborators of Chongruo 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 Chongruo Wu. Chongruo Wu 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:

11 of 11 papers shown

1.

Zhao, Zixu, Jianxiong Gao, Chongruo Wu, et al.. (2025). VideoSAM: Open-World Video Segmentation. 11155–11161.

2.

Zhang, Hang, Chongruo Wu, Zhongyue Zhang, et al.. (2022). ResNeSt: Split-Attention Networks. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2735–2745. 629 indexed citations breakdown →

3.

Wu, Chongruo, et al.. (2021). Predicting ASD diagnosis in children with synthetic and image-based eye gaze data. Signal Processing Image Communication. 94. 116198–116198. 41 indexed citations

4.

Wu, Chongruo, et al.. (2021). Machine Learning Based Autism Spectrum Disorder Detection from Videos. PubMed. 2020. 1–6. 18 indexed citations

5.

Zhu, Yi, et al.. (2021). A Unified Efficient Pyramid Transformer for Semantic Segmentation. 2667–2677. 27 indexed citations

6.

Zhu, Yi, Zhongyue Zhang, Chongruo Wu, et al.. (2021). Improving Semantic Segmentation via Efficient Self-Training. IEEE Transactions on Pattern Analysis and Machine Intelligence. 46(3). 1589–1602. 64 indexed citations

7.

Gu, Jing, Qingyang Wu, Chongruo Wu, Weiyan Shi, & Zhou Yu. (2021). PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation. 305–313. 4 indexed citations

8.

Cheng, Guangliang, et al.. (2019). Recognizing road from satellite images by structured neural network. Neurocomputing. 356. 131–141. 13 indexed citations

9.

Wu, Chongruo, et al.. (2019). Predicting Autism Diagnosis using Image with Fixations and Synthetic Saccade Patterns. PubMed. 2019. 647–650. 16 indexed citations

10.

Zhu, Xinge, et al.. (2019). Not All Areas Are Equal: Transfer Learning for Semantic Segmentation via Hierarchical Region Selection. 4355–4364. 46 indexed citations

11.

Liu, Xuanqing, Yao Li, Chongruo Wu, & Cho‐Jui Hsieh. (2018). Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network. arXiv (Cornell University). 8 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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