Chengyao Shen

783 total citations · 1 hit paper
8 papers, 454 citations indexed

About

Chengyao Shen is a scholar working on Computer Vision and Pattern Recognition, Cognitive Neuroscience and Human-Computer Interaction. According to data from OpenAlex, Chengyao Shen has authored 8 papers receiving a total of 454 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Vision and Pattern Recognition, 4 papers in Cognitive Neuroscience and 4 papers in Human-Computer Interaction. Recurrent topics in Chengyao Shen's work include Visual Attention and Saliency Detection (5 papers), Gaze Tracking and Assistive Technology (4 papers) and Face Recognition and Perception (3 papers). Chengyao Shen is often cited by papers focused on Visual Attention and Saliency Detection (5 papers), Gaze Tracking and Assistive Technology (4 papers) and Face Recognition and Perception (3 papers). Chengyao Shen collaborates with scholars based in Singapore, China and United States. Chengyao Shen's co-authors include Qi Zhao, Xun Huang, Xavier Boix, Kok Kiong Tan, Alex Tiong Heng Sia, Shuang Yu, Shuzhi Sam Ge, Hongsheng He, Liyuan Li and Joo‐Hwee Lim and has published in prestigious journals such as Pattern Recognition, Neurocomputing and IEEE Transactions on Multimedia.

In The Last Decade

Chengyao Shen

8 papers receiving 446 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.

SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks

2015 376 citations Xun Huang, Chengyao Shen et al. profile →

Peers

Chengyao Shen

CVPR

CN

HI

SS

AI

Boris Schauerte Germany

Elisa Sayrol Spain

Matthias Kümmerer Germany

Junting Pan China

Honghao Shan United States

Dicky N. Sihite United States

Qi Ma China

Stefan Mathe Sweden

B. Hidalgo-Sotelo United States

Boris Schauerte Germany

Chengyao Shen

202 ×0.5

CVPR

80 ×0.7

CN

126 ×1.1

HI

42 ×0.6

SS

24 ×0.8

AI

Citations per year, relative to Chengyao Shen Chengyao Shen (= 1×) peers Boris Schauerte

Countries citing papers authored by Chengyao Shen

Since Specialization

Citations

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

Fields of papers citing papers by Chengyao Shen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chengyao Shen

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

8 of 8 papers shown

1.

Li, Liyuan, et al.. (2017). Multi-layer linear model for top-down modulation of visual attention in natural egocentric vision. 16. 3470–3474. 2 indexed citations

2.

Huang, Xun, Chengyao Shen, Xavier Boix, & Qi Zhao. (2015). SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks. 262–270. 376 indexed citations breakdown →

3.

Shen, Chengyao, Xun Huang, & Qi Zhao. (2015). Predicting Eye Fixations on Webpage With an Ensemble of Early Features and High-Level Representations from Deep Network. IEEE Transactions on Multimedia. 17(11). 2084–2093. 25 indexed citations

4.

Shen, Chengyao, Xun Huang, & Qi Zhao. (2015). Attention in Low Resolution: Learning Proto-Object Representations with a Deep Network. Journal of Vision. 15(12). 898–898. 1 indexed citations

5.

Yu, Shuang, Kok Kiong Tan, Chengyao Shen, & Alex Tiong Heng Sia. (2014). Development of an ultrasound image processing approach to enable automated needle localisation for epidural anesthesia. 4(2). 137–137. 1 indexed citations

6.

Shen, Chengyao & Qi Zhao. (2014). Learning to predict eye fixations for semantic contents using multi-layer sparse network. Neurocomputing. 138. 61–68. 33 indexed citations

7.

Yu, Shuang, Kok Kiong Tan, Chengyao Shen, & Alex Tiong Heng Sia. (2013). Ultrasound guided automatic localization of needle insertion site for epidural anesthesia. National University of Singapore. 14. 985–990. 10 indexed citations

8.

Ge, Shuzhi Sam, Hongsheng He, & Chengyao Shen. (2011). Geometrically local embedding in manifolds for dimension reduction. Pattern Recognition. 45(4). 1455–1470. 6 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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