Chia-Lin Yu

National Yang Ming Chiao Tung University

Papers

Total Citations

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About

Chia-Lin Yu is a researcher at the forefront of efficient computer vision, specializing in domain-specific approximations for real-time object detection. Her work directly addresses the critical trade-off between accuracy and speed in autonomous systems, including advanced driver assistance, robotics, and self-driving vehicles. Yu’s most-cited paper, "Domain-Specific Approximation for Object Detection" (2018, 11 citations), introduces a novel framework that leverages domain knowledge to strategically reduce computational overhead without catastrophic accuracy loss. This contribution is pivotal for enabling faster, more responsive perception in resource-constrained environments. Beyond this flagship work, Yu’s research explores how tailored approximations can bridge the gap between theoretical model performance and practical deployment constraints. Her findings have implications for safety-critical applications where milliseconds matter, positioning her as a key voice in the push toward efficient, real-world AI. While her citation count reflects a focused, emerging impact, Yu’s work is foundational for engineers and researchers seeking to optimize deep learning models for edge devices and autonomous platforms.