Chenyang Huang

4.3k citations
13 papers · 2.7k indexed · 2 hit papers · h-index 8

Chenyang Huang

13 papers receiving 2.6k citations

Hit Papers

U2-Net: Going deeper with nested U-structure for salient ...1.4k20192026202120234008001.2k

Peers

Chenyang Huang
Comparison fields: 5 of 141
  • Computer Vision and Pattern Recognition 1.8k
  • Media Technology 388
  • Sensory Systems 113
  • Human-Computer Interaction 119
  • Industrial and Manufacturing Engineering 162
Replace Masood Dehghan with:
Masood Dehghan Singapore
Xuebin Qin China
Xiaowei Hu China
Martin Jägersand Canada
Pingping Zhang China
Muwei Jian China
Qiuping Jiang China
Changick Kim South Korea
Shanghua Gao China
Chenyang Huang relative to Masood Dehghan Singapore Masood Dehghan's profile →
Citations per field
00.5×1.5×
Masood Dehghan · 1×
Citations per year

Countries citing papers authored by Chenyang Huang

Since Specialization
Citations

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

Fields of papers citing papers by Chenyang Huang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 23 scholars most cited alongside Chenyang Huang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Chenyang Huang Line = papers co-authored together Chenyang Huang links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 20241
2 20232
3 20234
4 202216
5 202135
6 20212
7 202117
8
U2-Net: Going deeper with nested U-structure for salient object detectionbreakdown →
20201399
9 202019
10 20202
11 201922
12
BASNet: Boundary-Aware Salient Object Detectionbreakdown →
20191093
13 201884

About Chenyang Huang

Chenyang Huang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Human-Computer Interaction, having authored 13 papers that have together received 2.7k indexed citations. Recurring topics across this work include Topic Modeling (8 papers), Natural Language Processing Techniques (6 papers), Multimodal Machine Learning Applications (4 papers), Face Recognition and Perception (2 papers), Visual Attention and Saliency Detection (2 papers), Advanced Image and Video Retrieval Techniques (2 papers), Image and Signal Denoising Methods (1 paper) and Speech and dialogue systems (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (1.8k citations), Media Technology (388 citations) and Sensory Systems (113 citations). Chenyang Huang has collaborated with scholars based in Canada, China and United Kingdom. Frequent co-authors include Xuebin Qin, Masood Dehghan, Martin Jägersand, Osmar R. Zaı̈ane, Zichen Zhang, Zichen Zhang, Chao Gao, Amine Trabelsi, Nouha Dziri and Rui Wang. Their work appears in journals such as IEEE Access, Pattern Recognition and Machine Learning.

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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