About Me
Hi, my name is Qing YU. I am currently a research scientist at LY Corp. and a project researcher with The University of Tokyo, Japan. I am a member of Virtual Human Lab at LY Corp. I am also a member of Aizawa Labratory and working with Prof. Kiyoharu Aizawa. My research interest is in Computer Vision, including Image Recognition, Open-set Recognition and Classifier Adaption. I was a recipient of Research Fellowships for Young Scientists (DC1) (2020-2023) and I was a recipient of The University of Tokyo Fellowship (2018-2020). I am most skilled in: Python and PyTorch. Full CV is here.
Publications
Preprints
- Qing Yu, Go Irie and Kiyoharu Aizawa
Open-Set Domain Adaptation with Visual-Language Foundation Models
arXiv, 2023.
[Paper]
- Atsuyuki Miyai, Qing Yu, Go Irie, Kiyoharu Aizawa
Zero-Shot In-Distribution Detection in Multi-Object Settings Using Vision-Language Foundation Models
arXiv, 2023.
[Paper] [Code]
International Conferences
- Qing Yu, Mikihiro Tanaka and Kent Fujiwara
Exploring Vision Transformers for 3D Human Motion-Language Models with Motion Patches
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.
- Atsuyuki Miyai, Qing Yu, Go Irie and Kiyoharu Aizawa
LoCoOp: Few-Shot Out-of-Distribution Detection via Prompt Learning
Neural Information Processing Systems (NeurIPS), 2023.
[Paper] [Code]
- Jiafeng Mao, Qing Yu, Go Irie and Kiyoharu Aizawa
Noise-Avoidance Sampling for Annotation Missing Object Detection
IEEE International Conference on Image Processing (ICIP), 2023.
[Paper]
- Qing Yu and Kent Fujiwara
Frame-Level Label Refinement for Skeleton-Based Weakly-Supervised Action Recognition
AAAI Conference on Artificial Intelligence (AAAI), 2023.
[Paper] [Code]
- Atsuyuki Miyai, Qing Yu, Daiki Ikami, Go Irie and Kiyoharu Aizawa
Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation
Winter Conference on Applications of Computer Vision (WACV), 2023.
[Paper] [Code]
- Qing Yu, Daiki Ikami, Go Irie and Kiyoharu Aizawa
Self-Labeling Framework for Novel Category Discovery over Domains
AAAI Conference on Artificial Intelligence (AAAI), 2022.
[Paper] [Code]
- Jiafeng Mao, Qing Yu, Yoko Yamakata and Kiyoharu Aizawa
Noisy Annotation Refinement for Object Detection
British Machine Vision Conference (BMVC), 2021.
[Paper]
- Qing Yu, Atsushi Hashimoto and Yoshitaka Ushiku
Divergence Optimization for Noisy Universal Domain Adaptation
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.
[Paper] [Code]
- Qing Yu, Daiki Ikami, Go Irie and Kiyoharu Aizawa
Multi-Task Curriculum Framework for Open-Set Semi-Supervised Learning
European Conference on Computer Vision (ECCV), 2020.
[Paper] [Code]
- Qing Yu and Kiyoharu Aizawa
Unknown Class Label Cleaning for Learning with Open-Set Noisy Labels
IEEE International Conference on Image Processing (ICIP), 2020.
[Paper] [Code]
- Jiafeng Mao, Qing Yu and Kiyoharu Aizawa
Noisy Localization Annotation Refinement For Object Detection
IEEE International Conference on Image Processing (ICIP), 2020.
[Paper]
- Takumi Kawashima, Qing Yu, Akari Asai, Daiki Ikami and Kiyoharu Aizawa
The Aleatoric Uncertainty Estimation Using a Separate Formulation with Virtual Residuals
International Conference on Pattern Recognition (ICPR), 2020.
[Paper]
- Qing Yu and Kiyoharu Aizawa
Unsupervised Out-of-Distribution Detection by Maximum Classifier Discrepancy
IEEE International Conference on Computer Vision (ICCV), 2019.
[Paper] [Code]
- Qing Yu, Masashi Anzawa, Sosuke Amano, Makoto Ogawa and Kiyoharu Aizawa
Food Image Recognition by Personalized Classifier
IEEE International Conference on Image Processing (ICIP), 2018.
[Paper]
Journal
- Qing Yu, Go Irie and Kiyoharu Aizawa
Self-Labeling Framework for Open-Set Domain Adaptation with Few Labeled Samples
IEEE Transactions on Multimedia, 2023.
[Paper]
- Jiafeng Mao, Qing Yu and Kiyoharu Aizawa
Noisy Localization Annotation Refinement for Object Detection
IEICE TRANS INF SYST, 2021.
- Qing Yu, Masashi Anzawa, Sosuke Amano and Kiyoharu Aizawa
Personalized Food Image Classifier Considering Time-Dependent and Item-Dependent Food Distribution
IEICE TRANS INF SYST, 2019.
Domestic
Conferences
- Fourteen Papers
Experience
Worked on Motion Recognition and Generation with Virtual Human Lab.
Worked on Motion Recognition and Generation with Virtual Human Lab.
Worked on Motion Recognition with CV Lab.
Worked on Domain Adaptation.
An open course at UTokyo which offers the basics of CS and AI including deep learning.
Deep Learning application development.
Worked with Rinna Team.
Awards
Kaggle Master
SIGNATE Grandmaster
MIRU Best Student Paper
July 2022
MIRU Best Student Paper
August 2019
Data Science Challenge by FUJIFILM AI Academy Brain(s) Champion
July 2019
An OCR competition.
Prize: X Series FUJIFILM X100F
[Detail]
The 1st Tellus Satellite Challenge Champion
December 2018
A landslide detection competition.
Prize: 1,000,000 JPY
[Detail]
AIST Satellite Image Analysis Contest Champion
April 2018
Education
The University of Tokyo
PhD, Graduate School of Information Science and Technology
April 2020 - March 2023
The University of Tokyo
Master, Graduate School of Interdisciplinary Information Studies
April 2018 - March 2020
The University of Tokyo
Bachelor, Department of Information and Communication
April 2014 - March 2018
Gyosei International High School (Chiba, Japan)
October 2011 - March 2014
High School Affiliated to Fudan University (Shanghai, China)
September 2010 - October 2011
A Little More About Me
Alongside my research interests, some of my other interests and hobbies are:
- Animation
- Diving
- Basketball
- Movie
- Dota2