beat365系列講座菁英論壇第29期——Multisensory Machine Intelligence

報告題目(Title)：Multisensory Machine Intelligence

時間(Date & Time)：2024.05.13  6：30 - 8：00 pm

地點(Location)：二教207（燕園校區） Room 207, Teaching Building #2 (Yanyuan Campus)

主講人(Speaker)：Ruohan Gao (高若涵) from University of Maryland, College Park (UMD)

邀請人(Host)：Sheng Li (李勝)

報告摘要(Abstract)：

The future of Artificial Intelligence demands a paradigm shift towards multisensory perception—to systems that can digest ongoing multisensory observations, that can discover structure in unlabeled raw sensory data, and that can intelligently fuse useful information from different sensory modalities for decision making. While we humans perceive the world by looking, listening, touching, smelling, and tasting, traditional form of machine intelligence mostly focuses on a single sensory modality, particularly vision. My research aims to teach machines to see, hear, and feel like humans to perceive, understand, and interact with the multisensory world. In this talk, I will present my research of multisensory machine intelligence that studies two important aspects of the multisensory world: 1) multisensory objects, and 2) multisensory space. In both aspects, I will talk about how we design systems to reliably capture multisensory data, how we effectively model them with new differentiable simulation algorithms and deep learning models, and how we explore creative cross-modal/multi-modal applications with sight, sound, and touch.

主講人簡介(Bio)：

Dr. Ruohan Gao is an incoming assistant professor of the CS Department at University of Maryland, College Park, and currently a research scientist at Meta Reality Labs. Previously, he was a Postdoctoral Research Fellow working with Prof. Fei-Fei Li, Prof. Jiajun Wu, and Prof. Silvio Savarese in the Vision and Learning Lab at Stanford University. He obtained his Ph.D. advised by Prof. Kristen Grauman from The University of Texas at Austin. Ruohan mainly works in the fields of computer vision and machine learning with a specific focus on multisensory learning with sight, sound, and touch. His research has been recognized by the Michael H. Granof Award which is designated for UT Austin's Top 1 Doctoral Dissertation, the Google PhD Fellowship, the Adobe Research Fellowship, a Best Paper Award Runner Up at British Machine Vision Conference (BMVC) 2021, and a Best Paper Award Finalist at Conference on Computer Vision and Pattern Recognition (CVPR) 2019.