Deng-Ping Fan

27.3k citations
84 papers · 14.3k · 23 hit papers · h-index 43

Impact in

    • Visual Attention and Saliency Detection
    • Advanced Image and Video Retrieval Techniques
    • Advanced Neural Network Applications
    • Image Enhancement Techniques
    • Video Surveillance and Tracking Methods
    • Advanced Image Fusion Techniques

Papers in

    • Visual Attention and Saliency Detection 47
    • Advanced Image and Video Retrieval Techniques 29
    • Advanced Neural Network Applications 19
    • Image Enhancement Techniques 18
    • Image and Video Quality Assessment 10
    • Medical Image Segmentation Techniques 6
    • Multimodal Machine Learning Applications 6

Deng-Ping Fan

83 papers receiving 14.2k citations

Deng-Ping Fan's Hit Papers

VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning 2024 · 58 citations
580+2+4Years since publication10002.0k3.0k

Peers

Deng-Ping Fan
Comparison fields: 5 of 165
  • Computer Vision and Pattern Recognition 11.0k
  • Media Technology 2.1k
  • Sensory Systems 867
  • Human-Computer Interaction 562
  • Health Informatics 103
Replace Philip H. S. Torr with:
Philip H. S. Torr United Kingdom
Huazhu Fu China
Haibin Ling United States
Ming‐Ming Cheng China
Qibin Hou China
Xiaolei Huang United States
Yizhou Yu China
Jungong Han China
Dingwen Zhang China
Shi‐Min Hu China
Deng-Ping Fan relative to Philip H. S. Torr United Kingdom Philip H. S. Torr's profile →
Citations per field
00.5×2.9×
Philip H. S. Torr · 1×
Citations per year

Countries citing papers authored by Deng-Ping Fan

Since Specialization
Citations

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

Fields of papers citing papers by Deng-Ping Fan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Deng-Ping Fan. 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 Deng-Ping Fan. The network helps show where Deng-Ping Fan may publish in the future.

Co-authors

The 25 scholars most cited alongside Deng-Ping Fan, 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 Deng-Ping Fan Line = papers co-authored together Deng-Ping Fan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 84 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
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20213156
2
PVT v2: Improved baselines with pyramid vision transformer
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20221359
3
Structure-Measure: A New Way to Evaluate Foreground Maps
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20171249
4
EGNet: Edge Guidance Network for Salient Object Detection
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2019874
5
Inf-Net: Automatic COVID-19 Lung Infection Segmentation From CT Images
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2020821
6
Camouflaged Object Detection
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2020502
7
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
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2020496
8
Concealed Object Detection
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2021429
9
Shifting More Attention to Video Salient Object Detection
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2019390
10
Camouflaged Object Segmentation with Distraction Mining
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2021328
11
Contrast Prior and Fluid Pyramid Integration for RGBD Salient Object Detection
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2019316
12
Simultaneously Localize, Segment and Rank the Camouflaged Objects
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2021298
13
UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders
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2020278
14
Salient Object Detection via Integrity Learning
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2022273
15
JL-DCF: Joint Learning and Densely-Cooperative Fusion Framework for RGB-D Salient Object Detection
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2020255
16
Segment anything model for medical images?
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2023238
17
Mutual Graph Learning for Camouflaged Object Detection
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2021226
18
Uncertainty-Guided Transformer Reasoning for Camouflaged Object Detection
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2021206
19
Siamese Network for RGB-D Salient Object Detection and Beyond
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2021183
20
Polyp-PVT: Polyp Segmentation with Pyramid Vision Transformers
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2023170

About Deng-Ping Fan

Deng-Ping Fan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging and Human-Computer Interaction, having authored 84 papers that have together received 14.3k indexed citations. Recurring topics across this work include Visual Attention and Saliency Detection (47 papers), Advanced Image and Video Retrieval Techniques (29 papers), Advanced Neural Network Applications (19 papers), Image Enhancement Techniques (18 papers), Image and Video Quality Assessment (10 papers), Face Recognition and Perception (7 papers), Medical Image Segmentation Techniques (6 papers) and Multimodal Machine Learning Applications (6 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (11.0k citations), Media Technology (2.1k citations), Sensory Systems (867 citations), Human-Computer Interaction (562 citations) and Health Informatics (103 citations). Deng-Ping Fan has collaborated with scholars based in China, United Arab Emirates and Switzerland. Frequent co-authors include Ming‐Ming Cheng, Ling Shao, Ge-Peng Ji, Wenhai Wang, Xiang Li, Enze Xie, Ping Luo, Kaitao Song, Tong Lü and Ding Liang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Computational Visual Media, Science China Information Sciences, Medical Image Analysis and International Journal of Computer Vision.

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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