Chenxin Tao
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Aerospace Engineering
- Media Technology top 10%
- Industrial and Manufacturing Engineering
- Topics
- Advanced Neural Network Applications (5 papers)Domain Adaptation and Few-Shot Learning (5 papers)Multimodal Machine Learning Applications (4 papers)
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)The HKU Scholars Hub (University of Hong Kong)arXiv (Cornell University)
- Partner nations
- ChinaHong KongUnited States
In The Last Decade
Chenxin Tao
6 papers receiving 316 citations
Hit Papers
Peers
Comparison fields: 5 of 51
- Computer Vision and Pattern Recognition 246
- Artificial Intelligence 101
- Aerospace Engineering 63
- Media Technology 36
- Industrial and Manufacturing Engineering 25
Countries citing papers authored by Chenxin Tao
This map shows the geographic impact of Chenxin Tao'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 Chenxin Tao with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chenxin Tao more than expected).
Fields of papers citing papers by Chenxin Tao
This network shows the impact of papers produced by Chenxin Tao. 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 Chenxin Tao. The network helps show where Chenxin Tao may publish in the future.
Co-authorship network of co-authors of Chenxin Tao
This figure shows the co-authorship network connecting the top 25 collaborators of Chenxin Tao. A scholar is included among the top collaborators of Chenxin Tao 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 Chenxin Tao. Chenxin Tao 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 | 0 | |
| 3 | BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervisionbreakdown → | 147 |
| 4 | 28 | |
| 5 | 51 | |
| 6 | 29 | |
| 7 | Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation | 6 |
| 8 | 61 |
About Chenxin Tao
Chenxin Tao is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Media Technology, having authored 8 papers that have together received 322 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (5 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Multimodal Machine Learning Applications (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (246 citations), Media Technology (36 citations) and Computational Mathematics (2 citations). Chenxin Tao has collaborated with scholars based in China, Hong Kong and United States. Frequent co-authors include Jie Zhou, Jifeng Dai, Gao Huang, Xizhou Zhu, Yu Qiao, Lewei Lu, Hongyang Li, Jiwen Lu, Zhaoxiang Zhang and Chenyu Yang. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), The HKU Scholars Hub (University of Hong Kong) and arXiv (Cornell University).
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.