Di Hu
- Computer Vision and Pattern Recognition top 2%
- Artificial Intelligence top 5%
- Signal Processing top 2%
- Molecular Biology
- Industrial and Manufacturing Engineering top 5%
- Co-authors
- Xuelong LiFeiping NieYake WeiDong WangXiaokang PengXiaoqiang LuYingfeng ZhangYang Liu
- Topics
- Music and Audio Processing (23 papers)Speech and Audio Processing (20 papers)Video Analysis and Summarization (8 papers)
- Cited by
- Computer Vision and Pattern RecognitionSignal ProcessingIndustrial and Manufacturing Engineering
- Journals
- Angewandte Chemie International EditionIEEE Transactions on Pattern Analysis and Machine IntelligenceIEEE Transactions on Image Processing
- Partner nations
- ChinaUnited StatesHong Kong
In The Last Decade
Di Hu
57 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 123
- Computer Vision and Pattern Recognition 576
- Artificial Intelligence 283
- Signal Processing 269
- Molecular Biology 107
- Industrial and Manufacturing Engineering 102
Countries citing papers authored by Di Hu
This map shows the geographic impact of Di Hu'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 Di Hu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Di Hu more than expected).
Fields of papers citing papers by Di Hu
This network shows the impact of papers produced by Di Hu. 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 Di Hu. The network helps show where Di Hu may publish in the future.
Co-authorship network of co-authors of Di Hu
This figure shows the co-authorship network connecting the top 25 collaborators of Di Hu. A scholar is included among the top collaborators of Di Hu 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 Di Hu. Di Hu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 8 | |
| 6 | 3 | |
| 7 | 8 | |
| 8 | 0 | |
| 9 | 3 | |
| 10 | 1 | |
| 11 | 1 | |
| 12 | 49 | |
| 13 | 2 | |
| 14 | 32 | |
| 15 | Cross-Task Transfer for Multimodal Aerial Scene Recognition. | 2 |
| 16 | Multivariate Time Series Prediction Based on Optimized Temporal Convolutional Networks with Stacked Auto-encoders. | 5 |
| 17 | 106 | |
| 18 | Deep Co-Clustering for Unsupervised Audiovisual Learning. | 4 |
| 19 | 31 | |
| 20 | 1 |
About Di Hu
Di Hu is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Cognitive Neuroscience, having authored 62 papers that have together received 1.2k indexed citations. Recurring topics across this work include Music and Audio Processing (23 papers), Speech and Audio Processing (20 papers) and Video Analysis and Summarization (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (576 citations), Signal Processing (269 citations) and Industrial and Manufacturing Engineering (102 citations). Di Hu has collaborated with scholars based in China, United States and Hong Kong. Frequent co-authors include Xuelong Li, Feiping Nie, Yake Wei, Dong Wang, Xiaokang Peng, Xiaoqiang Lu, Yingfeng Zhang, Yang Liu, Geng Zhang and Yapeng Tian. Their work appears in journals such as Angewandte Chemie International Edition, IEEE Transactions on Pattern Analysis and Machine Intelligence 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.