Papers
3
Total Citations
23
H-Index
3
About
Yuxiang Xie is a researcher at the forefront of computer vision and energy-efficient AI hardware, with a focus on enabling intelligent perception for autonomous systems. His work bridges two critical domains: robust visual scene reconstruction and reconfigurable embedded processors for real-time object analysis. In his 2019 study on panorama stitching, Xie tackled the challenge of low-texture environments by integrating deep learning with iterative optimization, achieving high-resolution, wide field-of-view imagery essential for robot localization and environmental sensing. This work has garnered 12 citations, establishing a foundation for robust visual odometry. More recently, Xie has made significant contributions to edge AI, developing an energy-efficient reconfigurable processor for object detection and tracking. His 2022 paper, cited 6 times, introduces a novel architecture that supports online object learning—a crucial capability for adaptive drones and smart robots. By combining specialized neural network acceleration with flexible processing engines, Xie’s designs achieve high energy efficiency without sacrificing performance. His 2021 work, RAODAT, further refines this approach, demonstrating a path toward truly autonomous embedded intelligence. Xie’s research is pivotal for the next generation of power-constrained robotic systems that must learn and adapt in real time.
Research Focus
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Top Papers
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