Ming‐Ming Cheng
Impact in
- Computer Vision and Pattern Recognition top 0.01%
- Visual Attention and Saliency Detection
- Advanced Image and Video Retrieval Techniques
- Advanced Neural Network Applications
- Image Enhancement Techniques
- Video Surveillance and Tracking Methods
- Image and Video Quality Assessment
- Sensory Systems top 0.05%
- Olfactory and Sensory Function Studies
Papers in
-
- Advanced Image and Video Retrieval Techniques 90
- Visual Attention and Saliency Detection 72
- Advanced Neural Network Applications 64
- Advanced Vision and Imaging 41
- Multimodal Machine Learning Applications 22
- Advanced Image Processing Techniques 19
-
- Domain Adaptation and Few-Shot Learning 28
- Co-authors
- Shi‐Min Hu (19 shared papers)Philip H. S. Torr (19 shared papers)Qibin Hou (45 shared papers)Deng-Ping Fan (20 shared papers)Niloy J. Mitra (12 shared papers)Xiaolei Huang (7 shared papers)Yun Liu (26 shared papers)Jiangjiang Liu (9 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (44 papers)IEEE Transactions on Image Processing (16 papers)Computational Visual Media (7 papers)ACM Transactions on Graphics (7 papers)International Journal of Computer Vision (6 papers)
- Partner nations
- ChinaUnited KingdomUnited States
In The Last Decade
Ming‐Ming Cheng
218 papers receiving 33.6k citations
Ming‐Ming Cheng's Hit Papers
Peers
Comparison fields: 5 of 190
- Computer Vision and Pattern Recognition 27.4k
- Sensory Systems 2.6k
- Media Technology 4.7k
- Human-Computer Interaction 1.4k
- Cognitive Neuroscience 3.0k
Countries citing papers authored by Ming‐Ming Cheng
This map shows the geographic impact of Ming‐Ming Cheng'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 Ming‐Ming Cheng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming‐Ming Cheng more than expected).
Fields of papers citing papers by Ming‐Ming Cheng
This network shows the impact of papers produced by Ming‐Ming Cheng. 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 Ming‐Ming Cheng. The network helps show where Ming‐Ming Cheng may publish in the future.
Co-authors
The 25 scholars most cited alongside Ming‐Ming Cheng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 230 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Global contrast based salient region detection Hit paper breakdown → | 2011 | 2426 |
| 2 | Res2Net: A New Multi-Scale Backbone Architecture Hit paper breakdown → | 2019 | 2412 |
| 3 | Global Contrast Based Salient Region Detection Hit paper breakdown → | 2014 | 1983 |
| 4 | Attention mechanisms in computer vision: A survey Hit paper breakdown → | 2022 | 1617 |
| 5 | Structure-Measure: A New Way to Evaluate Foreground Maps Hit paper breakdown → | 2017 | 1331 |
| 6 | EGNet: Edge Guidance Network for Salient Object Detection Hit paper breakdown → | 2019 | 924 |
| 7 | Deeply Supervised Salient Object Detection with Short Connections Hit paper breakdown → | 2017 | 872 |
| 8 | A Simple Pooling-Based Design for Real-Time Salient Object Detection Hit paper breakdown → | 2019 | 855 |
| 9 | BING: Binarized Normed Gradients for Objectness Estimation at 300fps Hit paper breakdown → | 2014 | 791 |
| 10 | Struck: Structured Output Tracking with Kernels Hit paper breakdown → | 2015 | 774 |
| 11 | Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach Hit paper breakdown → | 2017 | 616 |
| 12 | Richer Convolutional Features for Edge Detection Hit paper breakdown → | 2017 | 599 |
| 13 | Visual attention network Hit paper breakdown → | 2023 | 596 |
| 14 | Richer Convolutional Features for Edge Detection Hit paper breakdown → | 2018 | 548 |
| 15 | Strip Pooling: Rethinking Spatial Pooling for Scene Parsing Hit paper breakdown → | 2020 | 545 |
| 16 | Camouflaged Object Detection Hit paper breakdown → | 2020 | 542 |
| 17 | Deeply Supervised Salient Object Detection with Short Connections Hit paper breakdown → | 2018 | 534 |
| 18 | LayerCAM: Exploring Hierarchical Class Activation Maps for Localization Hit paper breakdown → | 2021 | 530 |
| 19 | Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks Hit paper breakdown → | 2020 | 518 |
| 20 | Concealed Object Detection Hit paper breakdown → | 2021 | 457 |
About Ming‐Ming Cheng
Ming‐Ming Cheng is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Cognitive Neuroscience, Aerospace Engineering and Media Technology, having authored 230 papers that have together received 34.1k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (90 papers), Visual Attention and Saliency Detection (72 papers), Advanced Neural Network Applications (64 papers), Advanced Vision and Imaging (41 papers), Domain Adaptation and Few-Shot Learning (28 papers), Multimodal Machine Learning Applications (22 papers), Advanced Image Processing Techniques (19 papers) and Face Recognition and Perception (18 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (27.4k citations), Sensory Systems (2.6k citations), Media Technology (4.7k citations), Human-Computer Interaction (1.4k citations) and Cognitive Neuroscience (3.0k citations). Ming‐Ming Cheng has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Shi‐Min Hu, Philip H. S. Torr, Qibin Hou, Deng-Ping Fan, Niloy J. Mitra, Xiaolei Huang, Yun Liu, Jiangjiang Liu, Ali Borji and Shanghua Gao. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, Computational Visual Media, ACM Transactions on Graphics 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.