Xiaoliang Dai
- Computer Vision and Pattern Recognition top 0.5%
- Artificial Intelligence top 1%
- Electrical and Electronic Engineering
- Media Technology top 5%
- Computer Networks and Communications top 10%
- Topics
- Advanced Neural Network Applications (17 papers)Domain Adaptation and Few-Shot Learning (13 papers)Multimodal Machine Learning Applications (7 papers)
- Partner nations
- United StatesChinaIsrael
In The Last Decade
Xiaoliang Dai
40 papers receiving 2.0k citations
Hit Papers
Peers
Comparison fields: 5 of 118
- Computer Vision and Pattern Recognition 1.4k
- Artificial Intelligence 1.1k
- Electrical and Electronic Engineering 287
- Media Technology 105
- Computer Networks and Communications 102
Countries citing papers authored by Xiaoliang Dai
This map shows the geographic impact of Xiaoliang Dai'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 Xiaoliang Dai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoliang Dai more than expected).
Fields of papers citing papers by Xiaoliang Dai
This network shows the impact of papers produced by Xiaoliang Dai. 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 Xiaoliang Dai. The network helps show where Xiaoliang Dai may publish in the future.
Co-authorship network of co-authors of Xiaoliang Dai
This figure shows the co-authorship network connecting the top 25 collaborators of Xiaoliang Dai. A scholar is included among the top collaborators of Xiaoliang Dai 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 Xiaoliang Dai. Xiaoliang Dai 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 | 1 | |
| 4 | 5 | |
| 5 | 3 | |
| 6 | 3 | |
| 7 | 1 | |
| 8 | EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anythingbreakdown → | 74 |
| 9 | 3 | |
| 10 | 2 | |
| 11 | 12 | |
| 12 | 16 | |
| 13 | 28 | |
| 14 | Cross-Domain Adaptive Teacher for Object Detectionbreakdown → | 139 |
| 15 | 34 | |
| 16 | 48 | |
| 17 | 13 | |
| 18 | 168 | |
| 19 | 1 | |
| 20 | 3 |
About Xiaoliang Dai
Xiaoliang Dai is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Computer Graphics and Computer-Aided Design, having authored 44 papers that have together received 2.0k indexed citations. Recurring topics across this work include Advanced Neural Network Applications (17 papers), Domain Adaptation and Few-Shot Learning (13 papers) and Multimodal Machine Learning Applications (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.4k citations), Artificial Intelligence (1.1k citations) and Media Technology (105 citations). Xiaoliang Dai has collaborated with scholars based in United States, China and Israel. Frequent co-authors include BoRui Wu, Peizhao Zhang, Péter Vajda, Niraj K. Jha, Yuandong Tian, Hongxu Yin, Fei Sun, Yanghan Wang, Yangqing Jia and Yiming Wu. Their work appears in journals such as Optics Letters, Expert Systems with Applications and Remote Sensing.
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.