Wenpei Jiao

Papers

Total Citations

H-Index

About

Wenpei Jiao is a researcher at the forefront of computer vision and machine learning, with a particular focus on open-set recognition and long-tail learning in challenging real-world environments. Their most cited work, "Open-set recognition with long-tail sonar images" (2024, 29 citations), addresses a critical gap in automated image analysis: how to accurately identify known objects while robustly rejecting unknown ones, especially when data distributions are heavily imbalanced. This contribution is vital for sonar-based applications, such as underwater autonomous navigation and marine surveillance, where rare or novel targets must be distinguished from common clutter. By tackling the intersection of open-set and long-tail challenges, Jiao’s research provides a practical framework for deploying reliable AI in safety-critical domains. Their work not only advances theoretical understanding of distributional robustness but also offers tangible solutions for real-world sensor data. With a growing citation impact, Wenpei Jiao is establishing themselves as a key voice in making machine learning systems more trustworthy and effective in the wild.