题 目：Some Machine Learning Technologies for Image processing and Beyond
主讲人：蔡小昊 助理教授
单 位：University of Southampton
时 间：2026年5月9日 15:30
地 点：郑州校区九章学堂南楼C座219
摘 要：Machine/Deep learning technologies have revolutionised many fields including computer vision and image processing. Their success generally relies on big data. However, for the data scarcity scenarios like in medical imaging, their performance could drop significantly. Moreover, in many cases, they also lack generalisation (e.g. the cross-domain adaptation problem) and explanation (e.g. explainable AI). In this presentation, I will introduce some of our recent work on for example segmentation, classification and detection targeting those challenges, such as subspace feature representations for few-shot learning, multilevel explainable AI, cross-domain adaptation in point clouds linking to autonomous driving, 3D motion generation, large language models, etc.
简 介：Xiaohao Cai is an Assistant Professor in the School of Electronics and Computer Science at the University of Southampton. He received his PhD degree in mathematics from The Chinese University of Hong Kong in 2012. He afterwards was a Postdoctoral Researcher at the Department of Mathematics of the Technische Universitat Kaiserslautern in Germany. After that he was a Research Fellow (Wellcome Trust and Isaac Newton Trust) affiliated with the Department of Plant Sciences and Department of Applied Mathematics and Theoretical Physics at the University of Cambridge. Thenceforth, before joining Southampton, he was a Research Fellow in the Mullard Space Science Laboratory (MSSL) at University College London (UCL). He has broad multi-disciplinary research interests in applied mathematics, statistics, and computer science, with main focus and applications in image/signal/data processing, optimisation, machine learning and computer vision. He is Fellow of Advance HE in the UK. He has served as a peer reviewer of over 70 international journals and has published over 80 peer reviewed papers in journals and conferences such as SIAM and IEEE transactions. His recent work in AI for donkey identification has been featured by world-leading media like BBC and ITV.
