报告时间：2025年12月15日（星期一）10:00
报告地点：翡翠湖校区科教楼A1104室
报 告 人：Liang Zheng Associate Professor
工作单位：Australian National University
举办单位：计算机与信息学院
报告简介：
There are lots of different explorations of how to best train image generation models, from pixel-space generation, latent diffusion models, to representation learning alignment, representation auto-encoders and the recent JIT. In this talk, I will first discuss our recent works on end-to-end image generation which trains the VAE and generative model together. Then, I will draw comparisons and insights between our work and existing methods in terms of latent space quality, the usefulness of ImageNet training, and T2I pre-training.
报告人简介：
Dr. Liang Zheng is an Associate Professor at the Australian National University and a Research Scientist at Canva. He is interested in representation learning for perception and generation. He contributed many useful datasets and methods to the object re-identification field that were later used in wider domains. He is currently working on image generation in both aspects of pre-training and post-training. He is a Program Chair for ACM MM'24, AVSS'24, and ACM MM'28, and a General Chair for AVSS'27. He is a regular area chair for important conferences and an Associate Editor for TPAMI and ACM Computing Survey. He has bachelor degrees in Biology, Economics and a PhD degree in Computer Science from Tsinghua University.
