The Keynote Speakers

Prof. Ziwei Liu is currently a Nanyang Assistant Professor at Nanyang Technological University (NTU). Previously, he was a senior research fellow at the Chinese University of Hong Kong. Before that, Ziwei was a postdoctoral researcher at University of California, Berkeley, working with Prof. Stella Yu. Ziwei received his PhD from the Chinese University of Hong Kong in 2017, advised by Prof. Xiaoou Tang and Prof. Xiaogang Wang. During his PhD, Ziwei was fortunate to intern at Microsoft Research and Google Research, where he developed Microsoft Pix and Google Clips. His research revolves around computer vision, machine learning, and computer graphics. He has published over 80 papers (with more than 16,000 citations) on top-tier conferences and journals in relevant fields, including CVPR, ICCV, ECCV, NeurIPS, ICLR, SIGGRAPH, TOG, TPAMI, and Nature-Machine Intelligence. He serves as an Area Chair of ICCV 2021 and the Associate Editor of IET Computer Vision. He is the recipient of Microsoft Young Fellowship, Hong Kong PhD Fellowship, ICCV Young Researcher Award, and HKSTP Best Paper Award. He has won the championship in major computer vision competitions, including DAVIS Video Segmentation Challenge 2017, MSCOCO Instance Segmentation Challenge 2018, FAIR Self-Supervision Challenge 2019, and Video Virtual Try-on Challenge 2020. He is also the lead contributor of several renowned computer vision benchmarks and softwares, including CelebA, DeepFashion, MMFashion and MMHuman3D.

Title: Human-Centric Visual Generation and Editing

Abstract: Human-centric visual generation and editing have been a long-pursuing goal of computer vision and graphics, with extensive real-life applications. It is at the core of interactive intelligence. In this talk, I will discuss our work in visual generation and editing from three different aspects: data scaling infrastructure, foundation generative model and interactive editing framework. Our approach has shown its effectiveness and generalizability on a wide range of tasks.

Dr. Bo Han is currently an Assistant Professor of Computer Science and a Director of Trustworthy Machine Learning and Reasoning Group at Hong Kong Baptist University, and a BAIHO Visiting Scientist at RIKEN Center for Advanced Intelligence Project (RIKEN AIP). He was a Postdoc Fellow at RIKEN AIP (2019-2020). He received his Ph.D. degree in Computer Science from University of Technology Sydney (2015-2019). During 2018-2019, he was a Research Intern with the AI Residency Program at RIKEN AIP, working on trustworthy representation learning (e.g., Co-teaching and Masking). He is also working on causal representation learning (e.g., CausalAdv and CausalNL). He has co-authored a machine learning monograph, including Machine Learning with Noisy Labels (MIT Press). He has served as area chairs of NeurIPS, ICML and ICLR, senior program committees of AAAI, IJCAI and KDD, and program committees of AISTATS, UAI and CLeaR. He has also served as action editors of Transactions on Machine Learning Research and Neural Networks, a leading guest editor of Machine Learning Journal, and an editorial board reviewer of Journal of Machine Learning Research. He received the RIKEN BAIHO Award (2019), RGC Early CAREER Scheme (2020), MSRA StarTrack Program (2021) and Tencent AI Lab Focused Research Award (2022).

Title: Towards Trustworthy Learning and Reasoning under Noisy Data

Abstract: Trustworthy learning and reasoning are the emerging and critical topics in modern machine learning, since most real-world data are easily noisy, such as online transactions, healthcare, cyber-security, and robotics. Intuitively, trustworthy intelligent system should behave more human-like, which can learn and reason from noisy data. Therefore, in this talk, I will introduce trustworthy learning and reasoning from three human-inspired views, including reliability, robustness, and interaction. Specifically, reliability will consider uncertain cases, namely deep learning with noisy labels. Meanwhile, robustness will discuss adversarial conditions, namely deep learning with noisy (adversarial) features. Then, interaction will focus on the dynamic interaction between noisy labels and noisy features. Besides labels and features, I will discuss other noisy data, such as noisy domains, noisy demonstrations, and noisy graphs. Furthermore, I will introduce the newly established Trustworthy Machine Learning and Reasoning (TMLR) Group at Hong Kong SAR and Greater Bay Area.