Yu-Jhe Li

Yu-Jhe Li (李宇哲)

I'm currently an Applied Research Scientist at Adobe Firefly. I was a Research Scientist at Microsoft. I obtained my Doctor of Philosophy (Ph.D.) from Electrical and Computer Engineering at Carnegie Mellon University (CMU) under the guidance of Prof. Kris Kitani. Prior to my doctoral studies, I worked as a research associate with Kris Kitani at the Robotics Institute for one additional year. During my Ph.D. study, I have had the opportunity to spend two memorable summers with Meta Research, and one wonderful summer with Adobe Research.

In the past, I completed my Master of Science (M.Sc.) degree at National Taiwan University (國立台灣大學), where I was supervised by Prof. Yu-Chiang Frank Wang in the Vision and Learning Lab (VLL). I earned my Bachelor of Science (B.S.) degree from National Tsing Hua University (國立清華大學).

yujhe.jack [AT] gmail.com
Email / Curriculum Vitae (PDF) / Google Scholar / Github / Twitter

News

Feb. 2024: One paper is accepted to CVPR 2024.

Aug. 2023: I successfully defended my PhD!

Feb. 2023: One paper is accepted to CVPR 2023.

Feb. 2023: One paper is accepted to ICASSP 2023 special track.

Aug. 2022: Received Qualcomm Innocation Fellowship (only 19 teams in US, see this)

Jul. 2022: One paper is accepted to ECCV 2022.

Mar. 2022: Two papers are accepted to CVPR 2022.

Research

My research interests span the fields of Machine Learning (ML) and Computer Vision (CV), particularly:

Visual representation learning in machine perception (object detection, segmentation, tracking, re-identification).

Unsupervised domain adapted learning and data/label efficiency learning in machine perception.

Cross-modality visual representation learning for machine generation.

Publications (selected)

	Flexible Depth Completion for Sparse and Varying Point Densities Jinhyung Park, Yu-Jhe Li, Kris Kitani IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2024 Paper
	Multi-Person 3D Pose Estimation from Multi-View Uncalibrated Depth Cameras Yu-Jhe Li, Yan Xu, Rawal Khirodkar, Jinhyung Park, Kris Kitani Preprint. Arxiv, 2024 ArXiv
	Domain Adapted Visual Representation Learning for Machine Perception Yu-Jhe Li Ph.D. Thesis, Carnegie Mellon University, 2023 Committee members: Kris Kitani, Matthew O'Toole, Jun-Yan Zhu, Deva Ramanan Paper
	Azimuth Super-Resolution for FMCW Radar in Autonomous Driving Yu-Jhe Li, Shawn Hunt, Jinhyung Park, Matthew O'Toole, Kris Kitani IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023 Paper/ Dataset & Code
	3D-CLFusion: Fast Text-to-3D Rendering with Contrastive Latent Diffusion Yu-Jhe Li, Tao Xu, Ji Hou, Bichen Wu, Xiaoliang Dai, Albert Pumarola, Peizhao Zhang, Peter Vajda, Kris Kitani Preprint. Arxiv 2023 ArXiv
	ST-MVDNet++: Improve Vehicle Detection with Lidar-Radar Geometrical Augmentation via Self-Training Yu-Jhe Li, Matthew O'Toole, Kris Kitani IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 Paper
	3D-Aware Encoding for Style-based Neural Radiance Fields Yu-Jhe Li, Tao Xu, Bichen Wu, Ningyuan Zheng, Xiaoliang Dai, Albert Pumarola, Peizhao Zhang, Peter Vajda, Kris Kitani Preprint. Arxiv 2022 ArXiv
	Domain Adaptive Hand Keypoint and Pixel Localization in the Wild Takehiko Ohkawa, Yu-Jhe Li, Qichen Fu, Ryosuke Furuta, Kris M. Kitani, and Yoichi Sato European Conference on Computer Vision (ECCV), 2022 Arxiv
	Cross-Domain Adaptive Teacher for Object Detection Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris Kitani, Peter Vajda IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Project Page/ Paper/ Code
	Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection Yu-Jhe Li, Jinhyung Park, Matthew O'Toole, Kris Kitani IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Paper
	Visio-Temporal Attention for Multi- Camera Multi-Target Association. Yu-Jhe Li, Xinshuo Weng, Yan Xu, Kris Kitani IEEE International Conference on Computer Vision (ICCV), 2021 CVF Paper
	Wide-Baseline Multi-Camera Calibration using Person Re-Identification Yan Xu, Yu-Jhe Li, Xinshuo Weng, Kris Kitani IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021 CVF Paper
	Learning Shape Representations for Person Re-Identification under Clothing Change Yu-Jhe Li, Xinshuo Weng, Kris Kitani Winter Conference on Applications of Computer Vision (WACV), 2021 ArXiv (old version)/ CVF Paper
	Transforming Multi-Concept Attention into Video Summarization Yen-Ting Liu, Yu-Jhe Li, Yu-Chiang Frank Wang Asian Conference on Computer Vision (ACCV), 2020 ArXiv/ CVF Paper
	Cross-Resolution Adversarial Dual Network for Person Re-Identification and Beyond Yu-Jhe Li, Yun-Chun Chen, Yen-Yu Lin, Yu-Chiang Frank Wang (* indicates equal contribution) Preprint. Arxiv 2020 ArXiv
	Cross-Dataset Person Re-Identification via Unsupervised Pose Disentanglement and Adaptation Yu-Jhe Li, Ci-Siang Lin, Yan-Bo Lin, Yu-Chiang Frank Wang IEEE International Conference on Computer Vision (ICCV), 2019 ArXiv/ CVF Paper/ Code
	Recover and Identify: A Generative Dual Model for Cross-Resolution Person Re-Identification Yu-Jhe Li, Yun-Chun Chen, Yen-Yu Lin, Xiao-fei Du, Yu-Chiang Frank Wang (indicates equal contribution) IEEE International Conference on Computer Vision (ICCV)*, 2019 ArXiv/ CVF Paper
	Spot and Learn: A Maximum-Entropy Image Patch Sampler for Few-Shot Classification Wen-Hsuan Chu, Yu-Jhe Li, Jing-Cheng Chang, Yu-Chiang Frank Wang IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 CVF Paper
	Learning Resolution-Invariant Deep Representations for Person Re-Identification Yu-Jhe Li, Yun-Chun Chen, Xiao-fei Du, Yu-Chiang Frank Wang (indicates equal contribution) AAAI Conference on Artificial Intelligence (AAAI), 2019 (Oral Presentation)* ArXiv/ AAAI Paper
	Deep Reinforcement Learning for Playing 2.5D Fighting Games Yu-Jhe Li, Hsin-Yu Chang, Yu-Jing Lin, Po-Wei Wu, Yu-Chiang Frank Wang (indicates equal contribution) IEEE International Conference on Image Processing (ICIP)*, 2018 ArXiv/ IEEE Paper/ Code
	Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification Yu-Jhe Li, Fu-En Yang, Yen-Cheng Liu, Yu-Ying Yeh, Xiaofei Du, Yu-Chiang Frank Wang IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018 ArXiv/ Paper/ Code

Professional Activities

Reviewer or program committee:

ICLR 2023, CVPR 2023
ICLR 2022, CVPR 2022, ECCV 2022, NeurIPS 2022
AAAI 2021, WACV 2021, CVPR 2021, ICCV 2021
CVPR 2020, ECCV 2020, ACCV 2020

Education Experiences

	Ph.D., Electrical and Computer Engineering Carnegie Mellon University Pittsburgh, PA Sep. 2020 - Aug. 2023 Sep. 2019 - Aug. 2020 (Research Associate @ Robotics Institute)
	M.S., Communication Engineering (data science track) National Taiwan University Taipei, Taiwan Sep. 2017 - Jan. 2019
	B.S., Electrical Engineering and Computer Science National Tsing Hua University Taipei, Taiwan Sep. 2013 - Jan. 2017

Work Experiences

Applied Research Scientist
Adobe
San Jose, CA
Apr. 2024 - Current

Research Scientist
Microsoft
Redmond, WA
Dec. 2023 - Apr. 2024

Research Intern
Adobe
San Jose, CA
May. 2023 - Nov. 2023

Research Intern
Meta
Burlingame, CA
May. 2022 - Dec. 2022

Research Intern
Facebook (now Meta)
Menlo Park, CA
May. 2021 - Aug. 2021

Software Engineer Intern
Trend Micro
Taipei, Taiwan
July. 2017 - Aug. 2017

College Misc Experiences

	Exchange Student University of Minnesota - Twin Cities Minneapolis, MN, USA Aug. 2016 - Dec. 2016 Fall semester
	Exchange Student Fudan University Shanghai, China July. 2015 - Aug. 2015 Summer
	Exchange Student Tsinghua University Beijing, China July. 2014 - Aug. 2014 Summer