I am a Ph.D. from Beijing University of Chemical Technology, supervised by Prof. Fan Zhang and Prof. Wei Hu. I am also a visiting student at VLG, Singapore University of Technology and Design, where I have had a wonderful time and have been working with Prof. Jun Liu since March 2023. I am currently a Research Associate at Microsoft Research Asia, collaborating with Yangyu Huang. My research interests mainly focus on Visual Perception and Learning in the Open World, Large Multi-modal Models, AIGC.
🔥 News
- 2024.07: 🎉 One paper accept to ECCV 2024, Oral (Oral Paper Rate: 2.1% (188/8585).
- 2024.04: 🏅 LTGC is selected for oral presentation at CVPR 2024 (Oral Paper Rate: 0.78% (90/11532), Acceptance Rate: 23.6% (2719/11532)).
- 2024.02: 🎉 One paper accept to CVPR 2024.
📖 Experiences
- 2023.03 - 2024.03, Singapore University of Technology and Design, Visiting Student.
- Topic: Long-Tail Learning with LLMs
- 2019.09 - 2024.06, Beijing University of Chemical Technology, Joint Master’s and Ph.D.
- Topic: Image Recognition in the Open World
📝 Publications （* Equal Contribution）
LTRL: Boosting Long-tail Recognition via Reflective Learning
Oral, Top 2.1%
Reflective Learning, a plug-and-play method, boosts long-tail recognition by mimicking human thinking.
Qihao Zhao *, Yalun Dai *, Shen Lin, Wei Hu, Fan Zhang, Jun Liu
LTGC: Long-tail Recognition via Leveraging LLMs-driven Generated Content
Oral, Top 0.78%
A generative and tuning framework leveraging the knowledge of large language models for long-tail recognition.
Qihao Zhao *, Yalun Dai *, Hao Li, Wei Hu, Fan Zhang, Jun Liu
Project
MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition
A long-tail learning method for maximizing expert recognition diversity with minimum model variance.
Qihao Zhao, Chen Jiang, Wei Hu, Fan Zhang, Jun Liu
Code
MixPro: Data Augmentation with MaskMix and Progressive Attention Labeling for Vision Transformer
A novel data augmentation method designed for ViTs considering global information mixture and label space re-weighting.
Qihao Zhao, Yangyu Huang, Wei Hu, Fan Zhang, Jun Liu
A novel framework to address the challenge of noisy labels under long-tailed distribution.
Qihao Zhao, Fan Zhang, Wei Hu, Songhe Feng, Jun Liu
P-DIFF+: Improving Learning Classifier with Noisy Labels by Noisy Negative Learning Loss
A novel loss function, that mining knowledge from noisy samples to improve the robustness of models.
Qihao Zhao, Wei Hu, Yangyu Huang, Fan Zhang
💻 Service
- Co-suprivised Students:
- Yalun Dai (CVPR x 1, ECCV x 1, From BUCT, Now at NTU)
- Chen Jiang (ICCV x 1,From BUCT, Now at McGill University)
- Reviewer: NeurIPS, ICLR, T-CSVT