Assoc. Prof. Dr. Hao Lu, Huazhong University of Science and Technology, China
Assoc. Prof. Dr. Hao Lu is a researcher at Huazhong University of Science and Technology, China, specializing in computer vision and AI applications in agriculture. His work focuses on visual dense prediction, including semantic segmentation, depth estimation, and image matting. He has developed innovative upsampling operators such as IndexNet, SAPA, and FADE, as well as TasselNet, a widely used plant counting model. With over 80 publications, including 30+ CCF A-class papers and three ESI highly cited papers, his contributions have earned prestigious awards, including the Outstanding Paper Award from the European Agricultural Engineering Society.
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Summary:
Dr. Hao Lu is an exceptional early-career researcher with an impressive track record in AI-driven agriculture. His innovative research, high-impact publications, technological developments, and international collaborations position him as a strong candidate for the Research for Young Scientist Award. While expanding his global influence and increasing the real-world application of his research could enhance his impact, his current achievements make him highly competitive for the award.
🎓 Education
Hao Lu holds a faculty position as an Associate Professor at Huazhong University of Science and Technology. His academic background is rooted in computer vision and artificial intelligence, with a specialization in their applications in agriculture. His research expertise covers visual dense prediction, including semantic segmentation, depth estimation, and image matting, contributing to advancements in AI-driven agricultural technologies.
💼Experience
With a strong research portfolio, Hao Lu has made significant contributions to AI-driven plant phenotyping and crop monitoring systems. He has authored over 80 research papers, including more than 30 CCF A-class papers and three ESI highly cited papers. His collaborative efforts include working with leading experts such as Prof. Chunhua Shen from the University of Adelaide and Prof. Zhiguo Cao from Huazhong University of Science and Technology. His experience extends to pioneering AI applications in precision agriculture and smart farming.
🔬Research Focus
His research spans computer vision and AI applications in agriculture, specifically visual dense prediction and plant phenotyping. He has pioneered novel upsampling operators such as IndexNet, SAPA, and FADE, significantly improving image processing techniques. His TasselNet model is widely used in plant counting and phenotyping research, making a substantial impact on precision agriculture.
🛠️Skills
Hao Lu possesses expertise in computer vision, deep learning, and AI for agriculture. His work bridges fundamental AI advancements with real-world applications, including smart farming solutions. He has mentored students and contributed to high-impact research, with a citation count exceeding 4000 on Google Scholar. His research continues to drive innovations in crop monitoring, precision agriculture, and smart farming technologies.
🏆Awards
Hao Lu has received prestigious awards for his contributions to AI in agriculture. His research earned the Outstanding Paper Award from the European Agricultural Engineering Society. Additionally, his development of an automatic crop growth monitoring system was recognized with a bronze medal at the Geneva International Invention Exhibition.
📚 Publications
- SCAPE: A Simple and Strong Category-Agnostic Pose Estimator
Authors: Yujia Liang, Zixuan Ye, Wenze Liu, Hao Lu
Journal: [No source information available]
Year: 2024
- FADE: A Task-Agnostic Upsampling Operator for Encoder–Decoder Architectures
Authors: Hao Lu, Wenze Liu, Hongtao Fu, Zhiguo Cao
Journal: International Journal of Computer Vision
Year: 2025
- Bridging Real and Simulated Data for Cross-Spatial-Resolution Vegetation Segmentation with Application to Rice Crops
Authors: Yangmingrui Gao, Linyuan Li, Marie Weiss, Frédéric Baret, Shouyang Liu
Journal: ISPRS Journal of Photogrammetry and Remote Sensing
Year: 2024
- Counting Crowd by Weighing Counts: A Sequential Decision-Making Perspective
Authors: Hao Lu, Liang Liu, Hu Wang, Zhiguo Cao
Journal: IEEE Transactions on Neural Networks and Learning Systems
Year: 2024
- Vision Transformer Off-the-Shelf: A Surprising Baseline for Few-Shot Class-Agnostic Counting
Authors: Zhicheng Wang, Liwen Xiao, Zhiguo Cao, Hao Lu
Journal: [No source information available]
Year: 2024
- In-Context Matting
Authors: He Guo, Zixuan Ye, Zhiguo Cao, Hao Lu
Journal: [No source information available]
Year: 2024
- Unifying Automatic and Interactive Matting with Pretrained ViTs
Authors: Zixuan Ye, Wenze Liu, He Guo, Hao Lu, Zhiguo Cao
Journal: [No source information available]
Year: 2024
- SIERRA: A Robust Bilateral Feature Upsampler for Dense Prediction
Authors: Hongtao Fu, Wenze Liu, Yuliang Liu, Zhiguo Cao, Hao Lu
Journal: Computer Vision and Image Understanding
Year: 2023
Conclusion:
Dr. Hao Lu is an ideal candidate for the Research for Young Scientist Award. His work has already made a significant impact, and with continued advancements, he has the potential to become a global leader in AI-driven agricultural technologies.