Xiaojun Chang
Impact in
- Computer Vision and Pattern Recognition top 0.05%
- Advanced Image and Video Retrieval Techniques
- Multimodal Machine Learning Applications
- Video Surveillance and Tracking Methods
- Face and Expression Recognition
- Human Pose and Action Recognition
- Advanced Neural Network Applications
- Artificial Intelligence top 0.1%
- Domain Adaptation and Few-Shot Learning
- Anomaly Detection Techniques and Applications
Papers in
-
- Multimodal Machine Learning Applications 68
- Advanced Image and Video Retrieval Techniques 54
- Human Pose and Action Recognition 51
- Video Surveillance and Tracking Methods 41
- Video Analysis and Summarization 35
- Advanced Neural Network Applications 34
- Face and Expression Recognition 31
-
- Domain Adaptation and Few-Shot Learning 50
- Co-authors
- Yi Yang (42 shared papers)Feiping Nie (24 shared papers)Zhihui Li (24 shared papers)Chengqi Zhang (5 shared papers)Shirui Pan (10 shared papers)Alexander G. Hauptmann (26 shared papers)Po-Yao Huang (13 shared papers)Guodong Long (5 shared papers)
- Journals
- IEEE Transactions on Pattern Analysis and Machine Intelligence (19 papers)IEEE Transactions on Circuits and Systems for Video Technology (17 papers)IEEE Transactions on Image Processing (15 papers)IEEE Transactions on Neural Networks and Learning Systems (15 papers)Knowledge-Based Systems (13 papers)
- Partner nations
- ChinaAustraliaUnited States
In The Last Decade
Xiaojun Chang
272 papers receiving 12.7k citations
Xiaojun Chang's Hit Papers
Peers
Comparison fields: 5 of 187
- Computer Vision and Pattern Recognition 6.8k
- Artificial Intelligence 5.6k
- Media Technology 854
- Signal Processing 1.0k
- Computational Mathematics 39
Countries citing papers authored by Xiaojun Chang
This map shows the geographic impact of Xiaojun Chang'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 Xiaojun Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaojun Chang more than expected).
Fields of papers citing papers by Xiaojun Chang
This network shows the impact of papers produced by Xiaojun Chang. 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 Xiaojun Chang. The network helps show where Xiaojun Chang may publish in the future.
Co-authors
The 25 scholars most cited alongside Xiaojun Chang, 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 297 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networks Hit paper breakdown → | 2020 | 1103 |
| 2 | A Survey of Deep Active Learning Hit paper breakdown → | 2021 | 662 |
| 3 | A Comprehensive Survey of Neural Architecture Search Hit paper breakdown → | 2021 | 363 |
| 4 | A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition Hit paper breakdown → | 2019 | 318 |
| 5 | Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization Hit paper breakdown → | 2014 | 316 |
| 6 | Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition Hit paper breakdown → | 2019 | 305 |
| 7 | 2017 | 299 | |
| 8 | Semantic Pooling for Complex Event Analysis in Untrimmed Videos Hit paper breakdown → | 2016 | 290 |
| 9 | 2018 | 239 | |
| 10 | Self-Supervised Deep Correlation Tracking Hit paper breakdown → | 2020 | 235 |
| 11 | 2014 | 207 | |
| 12 | A Comprehensive Survey of Scene Graphs: Generation and Application Hit paper breakdown → | 2021 | 206 |
| 13 | 2016 | 203 | |
| 14 | 2016 | 202 | |
| 15 | 2015 | 200 | |
| 16 | 2017 | 190 | |
| 17 | 2019 | 176 | |
| 18 | 2017 | 160 | |
| 19 | 2021 | 150 | |
| 20 | 2019 | 145 |
About Xiaojun Chang
Xiaojun Chang is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing, Media Technology and Information Systems, having authored 297 papers that have together received 12.9k indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (68 papers), Advanced Image and Video Retrieval Techniques (54 papers), Human Pose and Action Recognition (51 papers), Domain Adaptation and Few-Shot Learning (50 papers), Video Surveillance and Tracking Methods (41 papers), Video Analysis and Summarization (35 papers), Advanced Neural Network Applications (34 papers) and Face and Expression Recognition (31 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (6.8k citations), Artificial Intelligence (5.6k citations), Media Technology (854 citations), Signal Processing (1.0k citations) and Computational Mathematics (39 citations). Xiaojun Chang has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Yi Yang, Feiping Nie, Zhihui Li, Chengqi Zhang, Shirui Pan, Alexander G. Hauptmann, Po-Yao Huang, Guodong Long, Lina Yao and Xiaodan Liang. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Image Processing, IEEE Transactions on Neural Networks and Learning Systems and Knowledge-Based Systems.
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.