Weijian Li

Applied Scientist, Amazon Robotics

weijianl@amazon.com

Bio

Hi, I'm an Applied Scientist at Amazon Robotics. My recent work focuses on vision, language, multimodal foundation models for robot perception and manipulations.

I received my Ph.D. degree from the Department of Computer Science, University of Rochester in 2022. My advisor was Professor Jiebo Luo. Before joining U of R, I obtained an M.S. degree in Electrical Engineering at the University of Southern California advised by Professor Alexander A. Sawchuk, and B.S. degrees from Beijing University of Posts and Telecommunications, and Queen Mary University of London. I’ve had the privilege of working with Dr. Shun Miao, Dr. Le Lu, Professor Gregory Hager, and many others.

News

  • [2024/11]: One paper on Causal Rule Generation accepted to IEEE BigData-2024. Congrats to authors!
  • [2024/10]: Had the privilege to share our works at Imaging-based Computational Biomedicine (ICB) Lab led by Prof. Yoshinobu Sato, Nara Institute of Science and Technology (NAIST), Nara, Japan.
  • [2024/03]: We will co-host the Retail AI Workshop at CVPR-2024, June 16-19, Seattle, WA. Check the schedule here. GroceryVision challenge is live!
  • [2023/08]: We will host the 1st Physical Retail AI Workshop at WACV-2024, Jan 3-7, Waikoloa, Hawaii. Come join us to learn and build the next generation of intelligent physical retail! [page]
  • [2023/07]: Two papers accepted to ICCV-2023. One on proposing the first unsupervised approach for self-driving attention prediction [paper][code]. The other on proposing a new formulation for structured information extraction(SIE) for document understanding [paper]. Congrats to the authors!
  • [2023/06]: One paper accepted to ICDH-2023. Check our work on predicting Parkinson's disease medication status [paper][code].
  • [2022/06]: Successfully defensed my thesis and will join Amazon Dash Cart as an Applied Scientist in July. Thank you all during my Ph.D. journey! Let's stay connected 🍻🍻🍻

Publications

Most recent publications on Google Scholar.
‡ indicates equal contribution.

  • Selected

Automated Bone Mineral Density Prediction and Fracture Risk Assessment using Plain Radiographs via Deep Learning

Chen-I Hsieh‡, Kang Zheng‡, Chihung Lin, Ling Mei, Le Lu, Weijian Li, Fang-Ping Chen, Yirui Wang, Xiaoyun Zhou, Fakai Wang, Guotong Xie, Jing Xiao, Shun Miao, Chang-Fu Kuo

Nature Communications, 12, Article number: 5472 (2021)

Paper

Structured Landmark Detection via Topology-Adapting Deep Graph Learning

Weijian Li, Yuhang Lu, Kang Zheng, Haofu Liao, Chihung Lin, Jiebo Luo, Chi-Tung Cheng, Jing Xiao, Le Lu, Chang-Fu Kuo, Shun Miao,

European Conference on Computer Vision (ECCV), 2020


Paper

Attentive Relational Networks for Mapping Images to Scene Graphs

Mengshi Qi‡, Weijian Li‡, Zhengyuan Yang, Yunhong Wang, Jiebo Luo,

IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019



Paper

Patch transformer for multi-tagging whole slide histopathology images

Weijian Li, Viet-Duy Nguyen, Haofu Liao, Matt Wilder, Ke Cheng, Jiebo Luo,

International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019, (Early Accept)


Paper

Mining the Relationship between Emoji Usage Patterns and Personality

Weijian Li‡, Yuxiao Chen, Tianran Hu, Jiebo Luo,

The International AAAI Conference on Web and Social Media (ICWSM), 2018



Paper

Service

Conference Reviewer: CVPR, ECCV, ICCV, WACV, MICCAI, ACMMM, AAAI, IJCAI
Journal Reviewer: TPAMI, TNNLS, Signal Processing Letters, Artificial Intelligence in Medicine

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