Hangbo Bao
-
- Multimodal Machine Learning Applications 4
- Advanced Image and Video Retrieval Techniques 1
- Handwritten Text Recognition Techniques 1
- Visual Attention and Saliency Detection 1
- Artificial Intelligence top 5%
- Natural Language Processing Techniques 6
- Topic Modeling 6
- Advanced Text Analysis Techniques 1
- Cryptography and Data Security 1
- Journals
- International Journal of Machine Learning and Cybernetics (1 paper)Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- ChinaIndiaUnited Kingdom
In The Last Decade
Hangbo Bao
8 papers receiving 416 citations
Hit Papers
Peers
Comparison fields: 5 of 74
- Computer Vision and Pattern Recognition 231
- Artificial Intelligence 279
- Health Informatics 5
- Media Technology 16
- Signal Processing 16
Countries citing papers authored by Hangbo Bao
This map shows the geographic impact of Hangbo Bao'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 Hangbo Bao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hangbo Bao more than expected).
Fields of papers citing papers by Hangbo Bao
This network shows the impact of papers produced by Hangbo Bao. 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 Hangbo Bao. The network helps show where Hangbo Bao may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Hangbo Bao, 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 | 2023 | 10 | |
| 2 | Image as a Foreign Language: BEIT Pretraining for Vision and Vision-Language Tasksbreakdown → | 2023 | 247 |
| 3 | 2022 | 42 | |
| 4 | 2022 | 9 | |
| 5 | 2021 | 115 | |
| 6 | 2021 | 4 | |
| 7 | MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers | 2020 | 3 |
| 8 | 2019 | 2 |
About Hangbo Bao
Hangbo Bao is a scholar working on Health Informatics, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 8 papers that have together received 432 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (6 papers), Topic Modeling (6 papers), Multimodal Machine Learning Applications (4 papers), Advanced Image and Video Retrieval Techniques (1 paper), Advanced Text Analysis Techniques (1 paper), Cryptography and Data Security (1 paper), Handwritten Text Recognition Techniques (1 paper) and Visual Attention and Saliency Detection (1 paper). The work is most often cited by research in Computer Vision and Pattern Recognition (231 citations), Artificial Intelligence (279 citations) and Health Informatics (5 citations). Hangbo Bao has collaborated with scholars based in China, India and United Kingdom. Frequent co-authors include Furu Wei, Shaohan Huang, Li Dong, Wenhui Wang, Wenhui Wang, Saksham Singhal, Dong Li, Zhiliang Peng, Johan Björck and Subhojit Som. Their work appears in journals such as International Journal of Machine Learning and Cybernetics, Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) and Neural Information Processing 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.