Yuanyuan Liao
Papers
1
Total Citations
5
H-Index
1
About
Yuanyuan Liao is a computer vision researcher whose work pushes the boundaries of object detection by addressing one of the field’s most persistent challenges: occlusion. In her highly cited 2022 paper, “Detection Beyond What and Where: A Benchmark for Detecting Occlusion State,” Liao introduced a novel framework that goes beyond simply identifying and localizing objects—her benchmark enables models to reason about whether an object is partially hidden and, crucially, to infer its occlusion state. This contribution is foundational for applications in autonomous driving, surveillance, and robotics, where understanding occluded objects can mean the difference between safe navigation and collision. While her citation count is still growing, her work has already been recognized for its conceptual leap: rather than treating occlusion as a nuisance to be ignored, Liao’s benchmark makes it a core part of the detection task. Her research is a must-read for students and engineers seeking to build more robust perception systems that see not just what is visible, but what is hidden.
Research Focus
Key Achievements
Top Papers
- 1