Chong‐Wah Ngo
- Computer Vision and Pattern Recognition top 0.05%
- Advanced Image and Video Retrieval Techniques 146
- Video Analysis and Summarization 112
- Image Retrieval and Classification Techniques 89
- Multimodal Machine Learning Applications 71
- Human Pose and Action Recognition 29
- Advanced Vision and Imaging 22
- Signal Processing top 0.5%
- Music and Audio Processing 20
- Artificial Intelligence top 0.5%
- Domain Adaptation and Few-Shot Learning 19
- Media Technology top 1%
- Human-Computer Interaction top 5%
Chong‐Wah Ngo
243 papers receiving 7.0k citations
Hit Papers
Peers
Comparison fields: 5 of 150
- Computer Vision and Pattern Recognition 6.2k
- Signal Processing 930
- Artificial Intelligence 1.9k
- Media Technology 385
- Human-Computer Interaction 123
Countries citing papers authored by Chong‐Wah Ngo
This map shows the geographic impact of Chong‐Wah Ngo'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 Chong‐Wah Ngo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chong‐Wah Ngo more than expected).
Fields of papers citing papers by Chong‐Wah Ngo
This network shows the impact of papers produced by Chong‐Wah Ngo. 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 Chong‐Wah Ngo. The network helps show where Chong‐Wah Ngo may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Chong‐Wah Ngo, 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 | 3 | |
| 3 | 2025 | 3 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 5 | |
| 6 | 2023 | 28 | |
| 7 | 2023 | 11 | |
| 8 | 2021 | 18 | |
| 9 | 2021 | 9 | |
| 10 | 2020 | 0 | |
| 11 | 2020 | 64 | |
| 12 | VireoJD-MM @ TRECVid 2019: Activities in Extended Video (ActEV). | 2019 | 1 |
| 13 | 2018 | 71 | |
| 14 | 2018 | 6 | |
| 15 | Object pooling for multimedia event detection and evidence localization | 2016 | 2 |
| 16 | 2016 | 5 | |
| 17 | VIREO @ TRECVID 2016: Multimedia Event Detection, Ad-hoc Video Search, Video-to-Text Description | 2016 | 2 |
| 18 | 2016 | 5 | |
| 19 | Automatic Generation of Semantic Fields for Annotating Web Images | 2010 | 1 |
| 20 | Motion Driven Approaches to Shot Boundary Detection, Low-Level Feature Extraction and BBC Rushes Characterization at TRECVID 2005. | 2005 | 6 |
About Chong‐Wah Ngo
Chong‐Wah Ngo is a scholar working on Computer Vision and Pattern Recognition, Signal Processing and Artificial Intelligence, having authored 254 papers that have together received 7.4k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (146 papers), Video Analysis and Summarization (112 papers), Image Retrieval and Classification Techniques (89 papers), Multimodal Machine Learning Applications (71 papers), Human Pose and Action Recognition (29 papers), Advanced Vision and Imaging (22 papers), Music and Audio Processing (20 papers) and Domain Adaptation and Few-Shot Learning (19 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (6.2k citations), Signal Processing (930 citations) and Artificial Intelligence (1.9k citations). Chong‐Wah Ngo has collaborated with scholars based in Hong Kong, China and Singapore. Frequent co-authors include Yu–Gang Jiang, Jun Yang, Xiao Wu, Wan‐Lei Zhao, Hao Zhang, Ting-Chuen Pong, Jingjing Chen, Ting Yao, Alexander G. Hauptmann and Hung‐Khoon Tan. Their work appears in journals such as IEEE Transactions on Image Processing, IEEE Transactions on Medical Imaging and ACM Computing Surveys.
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.