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  / 
CV (Dec 2023)  / 
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.
|
|
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
|
|
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
|
|
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
|
|