Gangshan Wu

7.4k total citations · 4 hit papers
156 papers, 3.7k citations indexed

About

Gangshan Wu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology. According to data from OpenAlex, Gangshan Wu has authored 156 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 114 papers in Computer Vision and Pattern Recognition, 43 papers in Artificial Intelligence and 15 papers in Media Technology. Recurrent topics in Gangshan Wu's work include Advanced Image and Video Retrieval Techniques (44 papers), Human Pose and Action Recognition (30 papers) and Advanced Vision and Imaging (26 papers). Gangshan Wu is often cited by papers focused on Advanced Image and Video Retrieval Techniques (44 papers), Human Pose and Action Recognition (30 papers) and Advanced Vision and Imaging (26 papers). Gangshan Wu collaborates with scholars based in China, Hong Kong and United States. Gangshan Wu's co-authors include Limin Wang, Tongwei Ren, Yutao Cui, Jie Liu, Cheng Jiang, Jie Tang, Ran Ju, Zhan Tong, Yuting Tang and Wenjie Zhang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing and Pattern Recognition.

In The Last Decade

Gangshan Wu

139 papers receiving 3.6k citations

Hit Papers

align trajectories sort by overperformance

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.

MixFormer: End-to-End Tracking with Iterative Mixed Attention

2022 464 citations Limin Wang, Gangshan Wu et al. profile →
Residual Feature Aggregation Network for Image Super-Resolution

2020 416 citations Jie Liu, Jie Tang et al. profile →
Depth saliency based on anisotropic center-surround difference

2014 355 citations Ran Ju, Wenjing Geng et al. profile →
TDN: Temporal Difference Networks for Efficient Action Recognition

2021 310 citations Limin Wang, Gangshan Wu et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Gangshan Wu China	28	2.9k	882	518	266	223	156	3.7k
Shuang Xu China	27	1.4k 0.5×	575 0.7×	959 1.9×	204 0.8×	391 1.8×	149	2.7k
Oncel Tuzel United States	31	3.4k 1.2×	943 1.1×	897 1.7×	589 2.2×	302 1.4×	59	4.5k
Yi-Zhe Song United Kingdom	38	4.3k 1.5×	1.8k 2.1×	278 0.5×	239 0.9×	212 1.0×	164	5.5k
Jianlong Fu China	31	4.4k 1.5×	2.0k 2.3×	682 1.3×	282 1.1×	235 1.1×	91	5.5k
Xavier Muñoz Spain	14	2.3k 0.8×	621 0.7×	619 1.2×	288 1.1×	118 0.5×	35	2.9k
R. Venkatesh Babu India	31	3.2k 1.1×	1.7k 2.0×	288 0.6×	163 0.6×	256 1.1×	149	3.9k
G. Csurka France	15	3.1k 1.1×	751 0.9×	561 1.1×	531 2.0×	112 0.5×	20	3.6k
Fengjun Lv United States	15	3.2k 1.1×	982 1.1×	602 1.2×	274 1.0×	300 1.3×	19	3.6k
Wujie Zhou China	36	3.4k 1.2×	453 0.5×	1.1k 2.1×	358 1.3×	87 0.4×	204	4.4k

Countries citing papers authored by Gangshan Wu

Since Specialization

Citations

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

Fields of papers citing papers by Gangshan Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Gangshan Wu

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

Xu, Yuheng, et al.. (2025). AutoLUT: LUT-Based Image Super-Resolution with Automatic Sampling and Adaptive Residual Learning. 23131–23140.

2.

Hou, Ruichao, Xingyuan Li, Tongwei Ren, et al.. (2025). HyPSAM: Hybrid Prompt-Driven Segment Anything Model for RGB-Thermal Salient Object Detection. IEEE Transactions on Circuits and Systems for Video Technology. 36(3). 2697–2712.

3.

Ren, Tongwei, et al.. (2024). CAGNet: a context-aware graph neural network for detecting social relationships in videos. 2(1). 3 indexed citations

4.

Wu, Gangshan, et al.. (2024). Weakly supervised instance segmentation via peak mining and filtering. IET Image Processing. 18(6). 1565–1578.

5.

Hou, Ruichao, et al.. (2024). Jointly modeling association and motion cues for robust infrared UAV tracking. The Visual Computer. 40(12). 8413–8424. 4 indexed citations

6.

Wu, Gangshan, et al.. (2024). Influences of season on cattle slurry compositions predicted by micro-NIR spectrometry. Microchemical Journal. 200. 110451–110451.

7.

Ren, Tongwei, et al.. (2023). Deep Video Understanding with Video-Language Model. 9551–9555. 1 indexed citations

8.

Zhou, Chang, Jie Liu, Jie Tang, & Gangshan Wu. (2023). Video Frame Interpolation with Densely Queried Bilateral Correlation. 1786–1794. 2 indexed citations

9.

Liu, Jie, Chao Chen, Jie Tang, & Gangshan Wu. (2023). From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-resolution. Proceedings of the AAAI Conference on Artificial Intelligence. 37(2). 1666–1674. 10 indexed citations

10.

Wu, Gangshan, et al.. (2023). Temporal Perceiver: A General Architecture for Arbitrary Boundary Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence. 45(10). 12506–12520. 8 indexed citations

11.

Wang, Limin, et al.. (2019). Translate-to-Recognize Networks for RGB-D Scene Recognition. 11828–11837. 36 indexed citations

12.

Ren, Tongwei, et al.. (2018). Adaptive saliency cuts. Multimedia Tools and Applications. 77(17). 22213–22230. 3 indexed citations

13.

Ju, Ran, et al.. (2014). Depth saliency based on anisotropic center-surround difference. 1115–1119. 355 indexed citations breakdown →

14.

Wu, Gangshan. (2011). Appraisal expression extraction based on parse tree structure. Jisuanji yingyong yanjiu. 2 indexed citations

15.

Pan, Zhigeng, Wenzhi Chen, Mingmin Zhang, Jianfeng Liu, & Gangshan Wu. (2009). Virtual Reality in the Digital Olympic Museum. IEEE Computer Graphics and Applications. 29(5). 91–95. 12 indexed citations

16.

Wu, Gangshan. (2006). Feature Selection for Neural Network-based Chinese Text Categorization. Jisuanji yingyong yanjiu. 2 indexed citations

17.

Wu, Gangshan. (2006). News Information Extraction for Web Resource. Jisuanji gongcheng.

18.

Wu, Gangshan. (2006). Skeleton Extraction Algorithm Using Reeb Graph Based on Facets. Jisuanji fangzhen. 2 indexed citations

19.

Wu, Gangshan. (2005). Design and Implementation of Web-based Full-text Search Engine. Jisuanji gongcheng. 1 indexed citations

20.

Wu, Gangshan. (2005). Information Extraction Technology for Web Forums. Jisuanji gongcheng.

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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