Chunluan Zhou

1.5k total citations · 1 hit paper
20 papers, 583 citations indexed

About

Chunluan Zhou is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Civil and Structural Engineering. According to data from OpenAlex, Chunluan Zhou has authored 20 papers receiving a total of 583 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Computer Vision and Pattern Recognition, 6 papers in Artificial Intelligence and 2 papers in Civil and Structural Engineering. Recurrent topics in Chunluan Zhou's work include Video Surveillance and Tracking Methods (10 papers), Advanced Neural Network Applications (9 papers) and Advanced Image and Video Retrieval Techniques (5 papers). Chunluan Zhou is often cited by papers focused on Video Surveillance and Tracking Methods (10 papers), Advanced Neural Network Applications (9 papers) and Advanced Image and Video Retrieval Techniques (5 papers). Chunluan Zhou collaborates with scholars based in China, United States and Singapore. Chunluan Zhou's co-authors include Junsong Yuan, Shenyuan Gao, Jun Zhang, Ming Yang, Youmei Zhang, Qian Zhang, Faliang Chang, Alex C. Kot, Jialian Wu and Gang Hua and has published in prestigious journals such as IEEE Transactions on Image Processing, Pattern Recognition and Neurocomputing.

In The Last Decade

Chunluan Zhou

19 papers receiving 571 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.

Generalized Relation Modeling for Transformer Tracking

2023 119 citations Shenyuan Gao, Chunluan Zhou et al. Rare & Special e-Zone (The Hong Kong University of Science and Technology) profile →

Peers

Chunluan Zhou

CVPR

AI

SRRQ

AE

Miran Pobar Croatia

Renshu Gu China

Guibo Zhu China

Csaba Beleznai Austria

Tsong-Yi Chen Taiwan

Kimin Yun South Korea

Vitaly Ablavsky United States

Jun‐Wei Hsieh Taiwan

Jiarui Xu China

Miran Pobar Croatia

Chunluan Zhou

430 ×0.8

CVPR

152 ×0.8

AI

32 ×0.5

SRRQ

25 ×0.5

AE

140 ×3.0

AE

Citations per year, relative to Chunluan Zhou Chunluan Zhou (= 1×) peers Miran Pobar

Countries citing papers authored by Chunluan Zhou

Since Specialization

Citations

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

Fields of papers citing papers by Chunluan Zhou

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chunluan Zhou

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

1.

Tu, Zhigang, et al.. (2025). FADE: A Dataset for Detecting Falling Objects Around Buildings in Video. IEEE Transactions on Information Forensics and Security. 20. 9746–9759. 1 indexed citations

2.

Zhou, Chunluan, et al.. (2024). M ² -RAAP: A Multi-Modal Recipe for Advancing Adaptation-based Pre-training towards Effective and Efficient Zero-shot Video-text Retrieval. 2156–2166.

3.

Xu, Yunqiu, Chunluan Zhou, Xin Yu, & Yi Yang. (2023). Cyclic Self-Training With Proposal Weight Modulation for Cross-Supervised Object Detection. IEEE Transactions on Image Processing. 32. 1992–2002. 7 indexed citations

4.

Gao, Shenyuan, Chunluan Zhou, & Jun Zhang. (2023). Generalized Relation Modeling for Transformer Tracking. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 18686–18695. 119 indexed citations breakdown →

5.

Liu, Ziyi, Zhenyu Wu, Chunluan Zhou, et al.. (2023). SOAR: Scene-debiasing Open-set Action Recognition. 10210–10220. 4 indexed citations

6.

Zhang, Zhengbo, Chunluan Zhou, & Zhigang Tu. (2022). Distilling Inter-Class Distance for Semantic Segmentation. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence. 1686–1692. 16 indexed citations

7.

Xu, Yunqiu, Chunluan Zhou, Xin Yu, Bin Xiao, & Yi Yang. (2021). Pyramidal Multiple Instance Detection Network With Mask Guided Self-Correction for Weakly Supervised Object Detection. IEEE Transactions on Image Processing. 30. 3029–3040. 38 indexed citations

8.

Yang, Yiding, et al.. (2021). Learning Dynamics via Graph Neural Networks for Human Pose Estimation and Tracking. 8070–8080. 54 indexed citations

9.

Wu, Jialian, Chunluan Zhou, Ming Yang, et al.. (2020). Temporal-Context Enhanced Detection of Heavily Occluded Pedestrians. 13427–13436. 47 indexed citations

10.

Wu, Jialian, Chunluan Zhou, Qian Zhang, Ming Yang, & Junsong Yuan. (2020). Self-Mimic Learning for Small-scale Pedestrian Detection. 2012–2020. 39 indexed citations

11.

Meng, Jingjing, et al.. (2020). Detecting spatiotemporal irregularities in videos via a 3D convolutional autoencoder. Journal of Visual Communication and Image Representation. 67. 102747–102747. 9 indexed citations

12.

Zhou, Chunluan & Junsong Yuan. (2019). Occlusion Pattern Discovery for Object Detection and Occlusion Reasoning. IEEE Transactions on Circuits and Systems for Video Technology. 1–1. 15 indexed citations

13.

Zhou, Chunluan, Ming Yang, & Junsong Yuan. (2019). Discriminative Feature Transformation for Occluded Pedestrian Detection. 9556–9565. 42 indexed citations

14.

Zhang, Youmei, Chunluan Zhou, Faliang Chang, & Alex C. Kot. (2019). A scale adaptive network for crowd counting. Neurocomputing. 362. 139–146. 6 indexed citations

15.

Dang, Kang, Chunluan Zhou, Zhigang Tu, et al.. (2018). Actor-Action Semantic Segmentation with Region Masks.. arXiv (Cornell University). 260. 1 indexed citations

16.

Zhang, Youmei, Chunluan Zhou, Faliang Chang, & Alex C. Kot. (2018). Multi-resolution attention convolutional neural network for crowd counting. Neurocomputing. 329. 144–152. 56 indexed citations

17.

Zhou, Chunluan & Junsong Yuan. (2018). Multi-label learning of part detectors for occluded pedestrian detection. Pattern Recognition. 86. 99–111. 27 indexed citations

18.

Zhou, Chunluan & Junsong Yuan. (2017). Multi-label Learning of Part Detectors for Heavily Occluded Pedestrian Detection. 96 indexed citations

19.

Zhou, Chunluan & Junsong Yuan. (2014). Non-rectangular Part Discovery for Object Detection. 51.1–51.13. 5 indexed citations

20.

Zhou, Chunluan & Jun Xiao. (2010). Cartoon capture by key-frame based contour tracking. Journal of Zhejiang University SCIENCE C. 12(1). 36–43. 1 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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