Dan Su

649 citations
24 papers · 452 · 1 hit paper · h-index 9

Impact in

Papers in

Dan Su

24 papers receiving 447 citations

Dan Su's Hit Papers

Multi-modal fusion network with multi-scale multi-path and cross-modal interactions for RGB-D salient object detection 2018 · 275 citations
2750+2+5Years since publication50100150200250

Peers

Dan Su
Comparison fields: 5 of 71
  • Computer Vision and Pattern Recognition 339
  • Human-Computer Interaction 89
  • Sensory Systems 33
  • Media Technology 50
  • Cognitive Neuroscience 85
Replace Konstantinos Rapantzikos with:
Konstantinos Rapantzikos Greece
Nabil Ouerhani Switzerland
Zijun Deng China
Junting Pan China
Shuhan Chen China
Mingchen Zhuge China
Sanyuan Zhao China
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Citations per field
00.5×1.5×2.3×
Konstantinos Rapantzikos · 1×
Citations per year

Countries citing papers authored by Dan Su

Since Specialization
Citations

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

Fields of papers citing papers by Dan Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 21 scholars most cited alongside Dan Su, 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 Dan Su Line = papers co-authored together Dan Su links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Multi-modal fusion network with multi-scale multi-path and cross-modal interactions for RGB-D salient object detection
Hit paper breakdown →
2018275
2 201932
3 200723
4 201821
5 201819
6 201916
7 202316
8 20179
9 20198
10 20215
11 20234
12 20204
13 20164
14 20143
15 20172
16 20192
17 20172
18
Study on the fire forces construction in the new rural
20101
19 20191
20 20231

About Dan Su

Dan Su is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction, Ophthalmology, Radiology, Nuclear Medicine and Imaging and Business and International Management, having authored 24 papers that have together received 452 indexed citations. Recurring topics across this work include Gaze Tracking and Assistive Technology (7 papers), Glaucoma and retinal disorders (6 papers), Visual Attention and Saliency Detection (5 papers), Advanced Neural Network Applications (4 papers), E-commerce and Technology Innovations (3 papers), Retinal Imaging and Analysis (3 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Advanced Computing and Algorithms (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (339 citations), Human-Computer Interaction (89 citations), Sensory Systems (33 citations), Media Technology (50 citations) and Cognitive Neuroscience (85 citations). Dan Su has collaborated with scholars based in China, Hong Kong and Canada. Frequent co-authors include Youfu Li, Hao Chen, Paul F.J. Eagles, Geoffrey Wall, Ziwei Ji, Pascale Fung, Wenliang Dai, Zihan Liu, Kun Jiang and Zhaoli Zhang. Their work appears in journals such as IEEE Transactions on Industrial Informatics, IEEE Transactions on Cybernetics, Environmental Management, Applied Soft Computing and Measurement Science and Technology.

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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