Hi there! I am a Ph.D. student at the University of Sydney, supervised by A/Prof. Chang Xu and Dr. Daochang Liu. I will be a visiting research fellow at the University of Oxford, working under the supervision of Prof. Philip Torr. I am honored to collaborate with Dr. Minjing Dong at CityU and Dr. Tao Huang at SJTU. I received my bachelor’s degree from HUST in 2017. My research focuses on Confidence Calibration and Uncertainty Estimation in Deep Learning and Large (Vision) Language Models.

Google Scholar / Github / CV

🔥 News

• 2025.05: 🎉🎉 One paper is accepted by ICML 2025.
• 2025.02: 🎉🎉 One paper is accepted by CVPR 2025.
• 2025.01: 🎉🎉 One paper is accepted by ICLR 2025.
• 2024.11: 🎉🎉 One paper is accepted by AAAI 2025.

📝 Publications

• Beyond One-Hot Labels: Semantic Mixing for Model Calibration
  Haoyang Luo, Linwei Tao, Minjing Dong, Chang Xu.
  ICML, 2025.

• Uncertainty Weighted Gradients for Model Calibration
  Jinxu Lin, Linwei Tao, Minjing Dong, Chang Xu.
  CVPR, 2025.

• KG-UQ: Knowledge Graph-Based Uncertainty Quantification for Long Text in Large Language Models
  Yingqing Yuan, Linwei Tao, Haohui Lu, Matloob Khushi, Imran Razzak, Mark Dras,Jian Yang, Usman Naseem.
  WWW Workshop, 2025.

• Diffusion Attribution Score: Evaluating Training Data Influence in Diffusion Model
  Jinxu Lin, Linwei Tao, Minjing Dong, Chang Xu.
  ICLR, 2025. (Spotlight, 5.1% of 11,500 submissions) [Paper]

• Feature Clipping for Uncertainty Calibration
  Linwei Tao, Minjing Dong, Chang Xu.
  AAAI, 2025. [Paper] [Code]

• GraphFusion: Integrating Multi-Level Semantic Information with Graph Computing forEnhanced 3D Instance Segmentation
  Lei Pan, Wuyang Luan, Yuan Zheng, Junhui Li, Linwei Tao, Chang Xu.
  Neurocomputing, 2024. [Paper] [Code]

• A Benchmark Study on Calibration
  Linwei Tao, Younan Zhu, Haolan Guo, Minjing Dong, Chang Xu.
  ICLR, 2024. [Paper] [Dataset] [Code] [Project Page] [Poster] [Slides]

• Dual Focal Loss for Calibration
  Linwei Tao, Minjing Dong, Chang Xu.
  ICML, 2023. [Paper] [Code] [Poster] [Slides]

• Calibrating a Deep Neural Network with Its Predecessors
  Linwei Tao, Minjing Dong, Daochang Liu, Changming Sun, Chang Xu.
  IJCAI, 2023. [Paper] [Code] [Playground] [Slides]

• Mitigating Object Hallucinations in Large Vision-Language Models via Attention Calibration
  Younan Zhu, Linwei Tao, Minjing Dong, Chang Xu.
  Preprint, 2025. [Paper]

• Consistency Calibration: Improving Uncertainty Calibration via Consistency among Perturbed Neighbors
  Linwei Tao, Haolan Guo, Minjing Dong, Chang Xu.
  Preprint, 2025. [Paper]

• Visual Imitation Learning with Calibrated Contrastive Representation
  Yunke Wang, Linwei Tao, Bo Du, Yutian Lin, Chang Xu.
  Preprint, 2024. [Paper]

📖 Education

• 2023.10 - Present, Ph.D in Computer Vision, University of Sydney
• 2021.10 - 2023.03, M.Phil in Computer Vision, University of Sydney
• 2020.02 - 2021.07, Master of Data Science, University of Sydney
• 2013.09 - 2017.06, B.Eng in Communication and Engineering, Huazhong University of Science and Technology

🧑‍🏫 Teaching

• 2024 S2, Head TA of OCMP5329 Deep Learning (Online)
• 2022 S1, 2023 S1, 2024 S1, Head TA of COMP5329 Deep Learning
• 2022 S2, 2023 S2, Head TA of HTIN5005 Applied Healthcare Data Science

💼 Internship

• 2015.06 - 2015.09, IOS developer, Ctrip.com
• 2014.06 - 2014.09, Frontend developer, Sunallies.com

🎖 Honors and Awards

• 2024.05, Google Cloud Research Credits Award ($2340 AUD)
• 2023.06, International Tuition Fee Scholarship, University of Sydney
• 2023.05, Faculty of Engineering Research Support Scholarship, University of Sydney
• 2015.09, Excellent Student Cadre, Huazhong University of Science and Technology
• 2014.06, Science and Technology Scholarship, Huazhong University of Science and Technology

💬 Invited Talks

• 2024.10, “ITCD: Image to Text Translation for Classification by Diffusion Models”, at Efficiency, Security, and Generalization of Multimedia Foundation Models Workshop @ ACM Multimedia 2024 [Link]
• 2023.05, “Dual Focal Loss for Calibration”, at AI-Time [Slides]
• 2023.02, “Calibrating a Deep Neural Network with Its Predecessors”, at University of Sydney, Deep Learning class [Slides]

💻 Academic Service

• Conference Reviewer: ICML, NeuIPS, AAAI, ICLR, CVPR, ICML, IJCAI, ACMMM, AISTATS
• Journal Reviewer: T-MM, TMLR, DAMI, TPAMI