Ou Ye

Xi'an University of Science and Technology

Papers

Total Citations

H-Index

About

Ou Ye’s research lies at the intersection of computer vision and social network security, with a focus on developing robust detection and classification algorithms for complex, real-world environments. In their early work, Ye advanced video scene classification by proposing an improved convolutional neural network (CNN) architecture designed to handle cluttered backgrounds—a persistent challenge in computer vision with applications ranging from video surveillance to robotics. This foundational paper has garnered 5 citations, establishing Ye’s expertise in deep learning for visual understanding. More recently, Ye has tackled the pressing issue of social bot detection, introducing a novel method that enhances graph neural networks to identify AI-controlled or human-operated social robot accounts. This 2024 work, already cited 4 times, addresses the scalability problem of analyzing massive social network graphs, offering a more efficient and accurate approach to safeguarding online communities. By bridging visual scene analysis and network security, Ye demonstrates a versatile ability to apply advanced neural architectures to diverse, high-impact problems. Their contributions are particularly valuable for researchers working on adversarial detection in social media and robust computer vision systems.