Chunhua Shen

449 total citations
10 papers, 298 citations indexed

About

Chunhua Shen is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computational Mechanics. According to data from OpenAlex, Chunhua Shen has authored 10 papers receiving a total of 298 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 5 papers in Artificial Intelligence and 3 papers in Computational Mechanics. Recurrent topics in Chunhua Shen's work include Advanced Image and Video Retrieval Techniques (5 papers), Face and Expression Recognition (5 papers) and Sparse and Compressive Sensing Techniques (3 papers). Chunhua Shen is often cited by papers focused on Advanced Image and Video Retrieval Techniques (5 papers), Face and Expression Recognition (5 papers) and Sparse and Compressive Sensing Techniques (3 papers). Chunhua Shen collaborates with scholars based in Australia, China and United States. Chunhua Shen's co-authors include Lei Wang, Luping Zhou, Junae Kim, Rufeng Zhang, Lei Li, Tao Kong, Xinlong Wang, Fumin Shen, Peng Wang and Hanzi Wang and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Adelaide Research & Scholarship (AR&S) (University of Adelaide).

In The Last Decade

Chunhua Shen

10 papers receiving 291 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chunhua Shen Australia	8	206	139	44	29	23	10	298
Yuanlong Shao China	5	178 0.9×	179 1.3×	59 1.3×	27 0.9×	26 1.1×	7	354
Pavan Kumar Mallapragada United States	5	215 1.0×	250 1.8×	32 0.7×	21 0.7×	13 0.6×	7	381
Bac Nguyen Belgium	9	188 0.9×	159 1.1×	20 0.5×	31 1.1×	26 1.1×	12	320
Marte A. Ramírez-Ortegón Germany	8	135 0.7×	142 1.0×	35 0.8×	29 1.0×	8 0.3×	13	326
Rongchun Zhao China	8	198 1.0×	88 0.6×	67 1.5×	19 0.7×	9 0.4×	57	321
Douglas R. Heisterkamp United States	10	226 1.1×	136 1.0×	40 0.9×	10 0.3×	9 0.4×	26	334
Hongping Cai China	9	182 0.9×	52 0.4×	37 0.8×	20 0.7×	15 0.7×	10	232
Huidong Liu China	9	176 0.9×	164 1.2×	24 0.5×	22 0.8×	26 1.1×	21	312
Shangqian Gao United States	9	280 1.4×	224 1.6×	17 0.4×	16 0.6×	21 0.9×	24	387

Countries citing papers authored by Chunhua Shen

Since Specialization

Citations

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

Fields of papers citing papers by Chunhua Shen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chunhua Shen

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

10 of 10 papers shown

Wang, Xinlong, Rufeng Zhang, Tao Kong, Lei Li, & Chunhua Shen. (2020). SOLOv2: Dynamic, Faster and Stronger.. arXiv (Cornell University). 56 indexed citations

Zhuang, Bohan, et al.. (2019). Training Compact Neural Networks via Auxiliary Overparameterization.. arXiv (Cornell University). 3 indexed citations

Wang, Lei, Luping Zhou, Chunhua Shen, Lingqiao Liu, & Huan Liu. (2014). A Hierarchical Word-Merging Algorithm with Class Separability Measure. IEEE Transactions on Pattern Analysis and Machine Intelligence. 36(3). 417–435. 10 indexed citations

Liu, Lingqiao, Lei Wang, & Chunhua Shen. (2011). A generalized probabilistic framework for compact codebook creation. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 1537–1544. 7 indexed citations

Shen, Chunhua, Peng Wang, Fumin Shen, & Hanzi Wang. (2011). {\cal U}Boost: Boosting with the Universum. IEEE Transactions on Pattern Analysis and Machine Intelligence. 34(4). 825–832. 41 indexed citations

Shen, Chunhua, Junae Kim, & Lei Wang. (2010). Scalable Large-Margin Mahalanobis Distance Metric Learning. IEEE Transactions on Neural Networks. 21(9). 1524–1530. 49 indexed citations

Zhou, Luping, Lei Wang, & Chunhua Shen. (2010). Feature Selection With Redundancy-Constrained Class Separability. IEEE Transactions on Neural Networks. 21(5). 853–858. 47 indexed citations

Shen, Chunhua, et al.. (2010). On the Dual Formulation of Boosting Algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence. 32(12). 2216–2231. 73 indexed citations

Shen, Chunhua, Michael J. Brooks, & Anton van den Hengel. (2005). Augmented particle filtering for efficient visual tracking. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 1. III–856. 8 indexed citations

10.

Shen, Chunhua, Anton van den Hengel, & Michael J. Brooks. (2005). Visual tracking via efficient kernel discriminant subspace learning. Adelaide Research & Scholarship (AR&S) (University of Adelaide). 1. II–590. 4 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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