Liqiang Nie
- Computer Vision and Pattern Recognition top 0.05%
- Multimodal Machine Learning Applications 122
- Advanced Image and Video Retrieval Techniques 102
- Video Analysis and Summarization 56
- Human Pose and Action Recognition 41
- Generative Adversarial Networks and Image Synthesis 40
- Artificial Intelligence top 0.05%
- Topic Modeling 79
- Domain Adaptation and Few-Shot Learning 43
- Information Systems top 0.05%
- Recommender Systems and Techniques 60
- Computational Mathematics top 2%
- Media Technology top 0.5%
Liqiang Nie
382 papers receiving 14.7k citations
Hit Papers
Peers
Comparison fields: 5 of 162
- Computer Vision and Pattern Recognition 9.0k
- Artificial Intelligence 6.6k
- Information Systems 3.5k
- Computational Mathematics 67
- Media Technology 736
Countries citing papers authored by Liqiang Nie
This map shows the geographic impact of Liqiang Nie'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 Liqiang Nie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Liqiang Nie more than expected).
Fields of papers citing papers by Liqiang Nie
This network shows the impact of papers produced by Liqiang Nie. 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 Liqiang Nie. The network helps show where Liqiang Nie may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Liqiang Nie, 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 | 0 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 1 | |
| 4 | 2024 | 6 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 0 | |
| 7 | 2024 | 7 | |
| 8 | 2024 | 15 | |
| 9 | 2024 | 6 | |
| 10 | 2024 | 3 | |
| 11 | 2024 | 2 | |
| 12 | 2024 | 9 | |
| 13 | 2024 | 3 | |
| 14 | 2023 | 14 | |
| 15 | 2023 | 2 | |
| 16 | 2023 | 3 | |
| 17 | 2021 | 68 | |
| 18 | 2021 | 10 | |
| 19 | 2020 | 51 | |
| 20 | 2019 | 30 |
About Liqiang Nie
Liqiang Nie is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems, having authored 410 papers that have together received 14.9k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (122 papers), Advanced Image and Video Retrieval Techniques (102 papers), Topic Modeling (79 papers), Recommender Systems and Techniques (60 papers), Video Analysis and Summarization (56 papers), Domain Adaptation and Few-Shot Learning (43 papers), Human Pose and Action Recognition (41 papers) and Generative Adversarial Networks and Image Synthesis (40 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (9.0k citations), Artificial Intelligence (6.6k citations) and Information Systems (3.5k citations). Liqiang Nie has collaborated with scholars based in China, Singapore and Australia. Frequent co-authors include Tat‐Seng Chua, Xuemeng Song, Xiangnan He, Hanwang Zhang, Wei Liu, Xiang Wang, Luming Zhang, Yinwei Wei, Meng Wang and David S. Rosenblum. Their work appears in journals such as IEEE Transactions on Multimedia, ACM Transactions on Information Systems, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Image Processing and ACM Transactions on Multimedia Computing Communications and Applications.
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.