Xiaojie Jin
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Media Technology top 10%
- Radiology, Nuclear Medicine and Imaging
- Computational Mathematics top 5%
- Topics
- Advanced Neural Network Applications (13 papers)Advanced Image and Video Retrieval Techniques (8 papers)Multimodal Machine Learning Applications (8 papers)
- Journals
- Expert Systems with ApplicationsIEEE Transactions on Neural Networks and Learning SystemsIEEE Transactions on Circuits and Systems for Video Technology
- Partner nations
- ChinaSingaporeUnited States
In The Last Decade
Xiaojie Jin
30 papers receiving 704 citations
Peers
Comparison fields: 5 of 104
- Computer Vision and Pattern Recognition 508
- Artificial Intelligence 231
- Media Technology 47
- Radiology, Nuclear Medicine and Imaging 43
- Computational Mathematics 35
Countries citing papers authored by Xiaojie Jin
This map shows the geographic impact of Xiaojie Jin'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 Xiaojie Jin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaojie Jin more than expected).
Fields of papers citing papers by Xiaojie Jin
This network shows the impact of papers produced by Xiaojie Jin. 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 Xiaojie Jin. The network helps show where Xiaojie Jin may publish in the future.
Co-authorship network of co-authors of Xiaojie Jin
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaojie Jin. A scholar is included among the top collaborators of Xiaojie Jin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Xiaojie Jin. Xiaojie Jin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 3 | |
| 3 | 2 | |
| 4 | 4 | |
| 5 | 0 | |
| 6 | 15 | |
| 7 | 57 | |
| 8 | 1 | |
| 9 | Neural Epitome Search for Architecture-Agnostic Network Compression | 3 |
| 10 | Deep Model Compression via Filter Auto-sampling. | 1 |
| 11 | 1 | |
| 12 | 23 | |
| 13 | 28 | |
| 14 | Dual Path Networks | 116 |
| 15 | Predicting Scene Parsing and Motion Dynamics in the Future | 18 |
| 16 | 72 | |
| 17 | 13 | |
| 18 | Multi-Path Feedback Recurrent Neural Network for Scene Parsing | 10 |
| 19 | 14 | |
| 20 | 5 |
About Xiaojie Jin
Xiaojie Jin is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Business and International Management, having authored 31 papers that have together received 723 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (13 papers), Advanced Image and Video Retrieval Techniques (8 papers) and Multimodal Machine Learning Applications (8 papers). The work is most often cited by research in Computational Mathematics (35 citations), Computer Vision and Pattern Recognition (508 citations) and Artificial Intelligence (231 citations). Xiaojie Jin has collaborated with scholars based in China, Singapore and United States. Frequent co-authors include Jiashi Feng, Shuicheng Yan, Yunpeng Chen, Huaxin Xiao, Zequn Jie, Jianan Li, Junjun Xiong, Chunyan Xu, Yunchao Wei and Xiaohui Shen. Their work appears in journals such as Expert Systems with Applications, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Circuits and Systems for Video Technology.
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.