Yuanwei Wu 吴元伟

Applied Scientist at Amazon
Seattle, WA

wuyuanwei2010 [at] gmail [dot] com
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About

I am an Applied Scientist at Amazon. My current research interests include large language models (LLMs), multimodal AI systems, and intelligent agents.

Before that, I was a research scientist in ObjectVideo Labs at Alarm.com, working on image understanding and video analytics for Smart Home using computer vision and machine learning techniques.

I received my Ph.D. degree in Electrical Engineering from The University of Kansas, advised by Professor Guanghui Wang in 2019. Before that, I received my Master's degree in Electrical Engineering from Tufts University. My research interests mainly focus on the area of computer vision and deep learning, particularly on efficient learning of deep models for object detection and tracking, face recognition, 3D point cloud analysis.

News

2025/04: [New] Still tuning on new AI projects ... ...
2021/03: Submitted two patents on smart home related applications.
2020/02: Started as Research Scientist at ObjectVideo Labs.
2019/12: I successfully defended my Ph.D. dissertation.
2019/12: “Self-Orthogonality Module for Learning Orthogonal Filters” accepted by WACV 2020.
2019/11: “Multi-scale deep feature learning network for object detection” accepted by Pattern Recognition.
2019/08: “Unsupervised Joint 3D Object Model Learning and 6D Pose Estimation” accepted by ICCV 2019 workshop.
2019/08: “Deep Feature Transfer for Low Resolution Image Classification” accepted by ICCV 2019 workshop.
2018/04: “Multi-scale object detection” accpeted by ICPR 2018.
2018/03: “BPGrad” accepted by CVPR 2018.

Publications

Book Chapter

Object Detection with Convolutional Neural Networks [PDF]
Kaidong Li, Wenchi Ma, Usman Sajid, Yuanwei Wu and Guanghui Wang
Deep Learning in Computer Vision: Principles and Applications, ISBN 9781138544420, 2020.

Journal Publications

MDFN: Multi-scale deep feature learning network for object detection [PDF]
Wenchi Ma, Yuanwei Wu, Feng Cen, Guanghui Wang
Pattern Recognition (PR), 2020.
Real-time Obstacle Detection and Tracking for Sense-and-Avoid Mechanism in UAVs [PDF]
Sushil Bharati, Yuanwei Wu, Yao Sui, Curtis Padgett and Guanghui Wang
IEEE Transactions on Intelligent Vehicles, Volume: 3 , Issue: 2, pp:185-197, 2018.
Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System [PDF] [Code]
Yuanwei Wu, Yao Sui, and Guanghui Wang
IEEE Access, Vol. 5, 23969 - 23978, 2017.

Conference Publications

Self-Orthogonality Module: A Network Architecture Plug-in for Learning Orthogonal Filters [PDF]
Ziming Zhang, Wenchi Ma, Yuanwei Wu and Guanghui Wang
The IEEE Winter Conference on Applications of Computer Vision (WACV), 2020.
Unsupervised Joint 3D Object Model Learning and 6D Pose Estimation for Depth-Based Instance Segmentation [PDF]
Yuanwei Wu, Tim K. Marks, Anoop Cherian, Siheng Chen, Chen Feng, Guanghui Wang and Alan Sullivan
The IEEE International Conference on Computer Vision (ICCV) 2019, 5th International Workshop on Recovering 6D Object Pose (R6D).
Unsupervised Deep Feature Transfer for Low Resolution Image Classification [PDF]
Yuanwei Wu, Ziming Zhang and Guanghui Wang
The IEEE International Conference on Computer Vision (ICCV) 2019, Workshop and Challenge on Real-World Recognition from Low-Quality Images and Videos (RLQ).
BPGrad: Towards Global Optimality in Deep Learning via Branch and Pruning [PDF] [Poster]
Ziming Zhang*, Yuanwei Wu*, and Guanghui Wang (* Equal contribution)
The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
MDCN: Multi-Scale, Deep Inception Convolutional Neural Networks for Efficient Object Detection [PDF] [Poster]
Wenchi Ma, Yuanwei Wu, Zongbo Wang and Guanghui Wang
International Conference on Pattern Recognition (ICPR), 2018.
Fast and Robust Object Tracking with Adaptive Detection [PDF]
Sushil Bharati, Soumyaroop Nandi, Yuanwei Wu, Yao Sui and Guanghui Wang
IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), 2016.

Research Experience

Graduate Research Assistant, The University of Kansas, Lawrence, KS, August 2015 - December 2017
Research Intern, Mitsubishi Electric Research Laboratories, Cambridge, MA, September 2018 - August 2019
Research Intern, Comcast Applied AI Labs, Washington, D.C., May 2018 - August 2018
Research Intern, Mitsubishi Electric Research Laboratories, Cambridge, MA, January 2017 - March 2017

Teaching Experience

Graduate Teaching Assistant, The University of Kansas
- EECS 268: Programming II, C++ (Fall 2017)
Teaching Assistant, Tufts University
- ES3: Introduction to Electrical Engineering (Fall 2012)
- ES4: Introduction to Digital Logic Circuits (Spring 2013)

Professional Activities

Conference Reviewer
- IEEE Computer Vision and Patter Recognition (CVPR), 2019-2025
- IEEE International Conference on Computer Vision (ICCV), 2019, 2021, 2023
- European Conference on Computer Vision (ECCV), 2020, 2022, 2024
- IEEE Winter Conference on Applications of Computer Vision (WACV), 2019, 2020
- International Conference on Pattern Recognition (ICPR), 2020
- International Joint Conference on Artificial Intelligence (IJCAI), 2019
- IEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2018, 2019
Journal Reviewer
- IEEE Transactions on Multimedia (TMM)
- Pattern Recognition (PR)
- IEEE Access
- IEEE Geoscience and Remote Sensing Letters (GRSL)
- IEEE Transactions on Medical Imaging (TMI)
- Applied Sciences
- Sensors