Zizhang Wu

739 total citations · 1 hit paper
27 papers, 424 citations indexed

About

Zizhang Wu is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Artificial Intelligence. According to data from OpenAlex, Zizhang Wu has authored 27 papers receiving a total of 424 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Computer Vision and Pattern Recognition, 10 papers in Aerospace Engineering and 6 papers in Artificial Intelligence. Recurrent topics in Zizhang Wu's work include Advanced Neural Network Applications (14 papers), Robotics and Sensor-Based Localization (8 papers) and Autonomous Vehicle Technology and Safety (5 papers). Zizhang Wu is often cited by papers focused on Advanced Neural Network Applications (14 papers), Robotics and Sensor-Based Localization (8 papers) and Autonomous Vehicle Technology and Safety (5 papers). Zizhang Wu collaborates with scholars based in China, Germany and Japan. Zizhang Wu's co-authors include Roland Tóth, Xiaoquan Wang, Jian Pu, Guilian Chen, Lei Wang, Fan Wang, Man Wang, Xianzhi Li, Weiwei Sun and Fan Wang and has published in prestigious journals such as Neurocomputing, IEEE Internet of Things Journal and Pattern Recognition Letters.

In The Last Decade

Zizhang Wu

23 papers receiving 413 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.

SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation

2020 263 citations Zizhang Wu, Roland Tóth et al. TU/e Research Portal profile →

Peers

Zizhang Wu

CVPR

AE

IME

MT

Hang Zhao China

Rui Qian China

Guoxin Zhang China

Xuanyao Chen China

Jae Shin Yoon United States

Chunmian Lin China

Jianhui Liu China

Wang Feng China

Jiageng Mao Hong Kong

Hang Zhao China

Zizhang Wu

278 ×0.8

CVPR

165 ×0.9

AE

61 ×1.0

AE

44 ×0.8

IME

23 ×0.5

MT

Citations per year, relative to Zizhang Wu Zizhang Wu (= 1×) peers Hang Zhao

Countries citing papers authored by Zizhang Wu

Since Specialization

Citations

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

Fields of papers citing papers by Zizhang Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Zizhang Wu

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

20 of 20 papers shown

1.

Li, Yuchen, Luxi Li, Zizhang Wu, et al.. (2024). MiningLLM: Towards Mining 5.0 via Large Language Models in Autonomous Driving and Smart Mining. IEEE Transactions on Intelligent Vehicles. 1–12. 4 indexed citations

2.

Li, Yuchen, Luxi Li, Zizhang Wu, et al.. (2024). UnstrPrompt: Large Language Model Prompt for Driving in Unstructured Scenarios. IEEE Journal of Radio Frequency Identification. 8. 367–375. 1 indexed citations

3.

Wu, Zizhang, et al.. (2024). FD3D: Exploiting Foreground Depth Map for Feature-Supervised Monocular 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 38(6). 6189–6197. 3 indexed citations

4.

Wu, Zizhang, Yunzhe Wu, Jian Pu, Xianzhi Li, & Xiaoquan Wang. (2023). Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence. 37(3). 2892–2900. 12 indexed citations

5.

Wu, Zizhang, et al.. (2023). ADD: An automatic desensitization fisheye dataset for autonomous driving. Engineering Applications of Artificial Intelligence. 126. 106766–106766. 3 indexed citations

6.

Wu, Zizhang, et al.. (2023). MonoPGC: Monocular 3D Object Detection with Pixel Geometry Contexts. 4842–4849. 16 indexed citations

7.

Wu, Zizhang, et al.. (2023). OCR-RTPS: an OCR-based real-time positioning system for the valet parking. Applied Intelligence. 53(14). 17920–17934. 4 indexed citations

8.

Li, Jun, Na Jiang, Han Zhang, et al.. (2023). Temporal Transformer Networks With Self-Supervision for Action Recognition. IEEE Internet of Things Journal. 10(14). 12999–13011. 5 indexed citations

9.

Wu, Zizhang, et al.. (2023). Graph-Segmenter: graph transformer with boundary-aware attention for semantic segmentation. Frontiers of Computer Science. 18(5). 10 indexed citations

10.

Wu, Zizhang, et al.. (2023). MVFusion: Multi-View 3D Object Detection with Semantic-aligned Radar and Camera Fusion. 2766–2773. 25 indexed citations

11.

Wu, Zizhang, et al.. (2022). Surround-View Fisheye BEV-Perception for Valet Parking: Dataset, Baseline and Distortion-Insensitive Multi-Task Framework. IEEE Transactions on Intelligent Vehicles. 8(3). 2037–2048. 8 indexed citations

12.

Wu, Zizhang, Zhigang Fan, Jizheng Wang, et al.. (2022). Monocular Fisheye Depth Estimation for Automated Valet Parking: Dataset, Baseline and Deep Optimizers. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). 1–7.

13.

Wu, Zizhang, et al.. (2022). Detecting owner-member relationship in fisheye camera system with graph convolution network. Pattern Recognition Letters. 155. 34–40.

14.

Wu, Zizhang, et al.. (2022). Complete solution for vehicle Re-ID in surround-view camera system. International Journal of Machine Learning and Cybernetics. 14(5). 1739–1749. 1 indexed citations

15.

Wu, Zizhang, et al.. (2021). DeepWORD: A GCN-Based Approach for Owner-Member Relationship Detection in Autonomous Driving. 1–6.

16.

Wu, Zizhang, et al.. (2021). Constructing infinite deep neural networks with flexible expressiveness while training. Neurocomputing. 487. 257–268. 3 indexed citations

17.

Feng, Songhe, et al.. (2021). L4Net: An anchor‐free generic object detector with attention mechanism for autonomous driving. IET Computer Vision. 15(1). 36–46. 9 indexed citations

18.

Wu, Zizhang, et al.. (2020). SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. TU/e Research Portal. 4289–4298. 263 indexed citations breakdown →

19.

Wu, Zizhang, et al.. (2020). PSDet: Efficient and Universal Parking Slot Detection. 290–297. 21 indexed citations

20.

Tong, Guofeng, et al.. (2012). An Omni-directional vSLAM based on spherical camera model and 3D modeling. 100. 4551–4556. 7 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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