Long Mai
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- Advanced Vision and Imaging 10
- Advanced Image Processing Techniques 8
- Visual Attention and Saliency Detection 7
- Advanced Image and Video Retrieval Techniques 6
- Generative Adversarial Networks and Image Synthesis 3
- Image and Video Quality Assessment 3
- Image Retrieval and Classification Techniques 3
- Media Technology top 1%
- Image Processing Techniques and Applications 4
- Signal Processing top 5%
- Sensory Systems top 5%
Long Mai
31 papers receiving 1.7k citations
Hit Papers
Peers
Comparison fields: 5 of 99
- Computer Vision and Pattern Recognition 1.5k
- Media Technology 364
- Computer Graphics and Computer-Aided Design 122
- Signal Processing 184
- Sensory Systems 61
Countries citing papers authored by Long Mai
This map shows the geographic impact of Long Mai'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 Long Mai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Long Mai more than expected).
Fields of papers citing papers by Long Mai
This network shows the impact of papers produced by Long Mai. 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 Long Mai. The network helps show where Long Mai may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Long Mai, 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 | 2025 | 0 | |
| 2 | 2024 | 2 | |
| 3 | 2022 | 9 | |
| 4 | 2022 | 3 | |
| 5 | 2022 | 1 | |
| 6 | 2021 | 112 | |
| 7 | BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images | 2020 | 5 |
| 8 | 2020 | 110 | |
| 9 | 2020 | 27 | |
| 10 | 2020 | 39 | |
| 11 | 2019 | 23 | |
| 12 | NLP-EYE: detecting memory corruptions via semantic-aware memory operation function identification | 2019 | 5 |
| 13 | MultiSeg: Semantically Meaningful, Scale-Diverse Segmentations From Minimal User Input | 2019 | 1 |
| 14 | 2018 | 34 | |
| 15 | 2017 | 8 | |
| 16 | Video Frame Interpolation via Adaptive Separable Convolutionbreakdown → | 2017 | 418 |
| 17 | 2017 | 296 | |
| 18 | 2015 | 23 | |
| 19 | 2013 | 119 | |
| 20 | 2011 | 38 |
About Long Mai
Long Mai is a scholar working on Computer Vision and Pattern Recognition, Media Technology, Computer Graphics and Computer-Aided Design, Artificial Intelligence and Sensory Systems, having authored 32 papers that have together received 1.8k indexed citations. Recurring topics across this work include Advanced Vision and Imaging (10 papers), Advanced Image Processing Techniques (8 papers), Visual Attention and Saliency Detection (7 papers), Advanced Image and Video Retrieval Techniques (6 papers), Image Processing Techniques and Applications (4 papers), Generative Adversarial Networks and Image Synthesis (3 papers), Image and Video Quality Assessment (3 papers) and Image Retrieval and Classification Techniques (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.5k citations), Media Technology (364 citations), Computer Graphics and Computer-Aided Design (122 citations), Signal Processing (184 citations) and Sensory Systems (61 citations). Long Mai has collaborated with scholars based in United States, South Korea and Australia. Frequent co-authors include Feng Liu, Simon Niklaus, Hailin Jin, Feng Liu, Yuzhen Niu, Oliver Wang, Jianming Zhang, Shuicheng Yan, Zhe Lin and Bac Le. Their work appears in journals such as ACM Transactions on Graphics, Sensors, Annals of Operations Research, Journal of Network and Systems Management and Pure (University of Bath).
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.