Ding Liang
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- Advanced Neural Network Applications 9
- Context-Aware Activity Recognition Systems 5
- Face and Expression Recognition 4
- Media Technology top 0.2%
- Artificial Intelligence top 0.5%
- Domain Adaptation and Few-Shot Learning 7
- Neurology top 5%
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- Energy Efficient Wireless Sensor Networks 9
- Security in Wireless Sensor Networks 4
- Distributed Sensor Networks and Detection Algorithms 4
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- Balance, Gait, and Falls Prevention 4
Ding Liang
32 papers receiving 5.4k citations
Hit Papers
Peers
Comparison fields: 5 of 156
- Computer Vision and Pattern Recognition 3.7k
- Media Technology 969
- Artificial Intelligence 1.4k
- Industrial and Manufacturing Engineering 279
- Neurology 232
Countries citing papers authored by Ding Liang
This map shows the geographic impact of Ding Liang'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 Ding Liang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ding Liang more than expected).
Fields of papers citing papers by Ding Liang
This network shows the impact of papers produced by Ding Liang. 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 Ding Liang. The network helps show where Ding Liang may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Ding Liang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 8 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 10 | |
| 5 | 2024 | 2 | |
| 6 | 2023 | 61 | |
| 7 | 2023 | 2 | |
| 8 | PVT v2: Improved baselines with pyramid vision transformerbreakdown → | 2022 | 1259 |
| 9 | 2022 | 4 | |
| 10 | Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutionsbreakdown → | 2021 | 3008 |
| 11 | PolarMask: Single Shot Instance Segmentation With Polar Representationbreakdown → | 2020 | 431 |
| 12 | 2020 | 192 | |
| 13 | 2013 | 2 | |
| 14 | 2013 | 0 | |
| 15 | 2012 | 1 | |
| 16 | 2012 | 47 | |
| 17 | An approach to retinal image segmentations using fuzzy clustering in combination with morphological filters | 2011 | 2 |
| 18 | 2011 | 24 | |
| 19 | 2009 | 26 | |
| 20 | 2008 | 1 |
About Ding Liang
Ding Liang is a scholar working on Computer Vision and Pattern Recognition, Physical Therapy, Sports Therapy and Rehabilitation and Computer Networks and Communications, having authored 39 papers that have together received 5.5k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (9 papers), Energy Efficient Wireless Sensor Networks (9 papers), Domain Adaptation and Few-Shot Learning (7 papers), Context-Aware Activity Recognition Systems (5 papers), Face and Expression Recognition (4 papers), Balance, Gait, and Falls Prevention (4 papers), Security in Wireless Sensor Networks (4 papers) and Distributed Sensor Networks and Detection Algorithms (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (3.7k citations), Media Technology (969 citations) and Artificial Intelligence (1.4k citations). Ding Liang has collaborated with scholars based in China, Hong Kong and Australia. Frequent co-authors include Ping Luo, Enze Xie, Wenhai Wang, Xiang Li, Tong Lü, Deng-Ping Fan, Ling Shao, Kaitao Song, Xuebo Liu and Chunhua Shen. Their work appears in journals such as Sensors, IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Instrumentation and Measurement, FEBS Journal and IEEE Transactions on Image Processing.
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.