智信讲坛第(101)期 Context in Analysis of Multimodal Social Behavior

学术报告

题目：Context in Analysis of Multimodal Social Behavior

报告人：Nicu Sebe

时间：2017年6月28日周四上午 9:30

地点：电信楼403

组织单位：计算机科学与技术系

邀请人：吴俊教授

报告人简介：

Nicu Sebe is a professor in the University of Trento, Italy, where he is the head of the Department of Information Engineering and Computer Science. He is leading the research in the areas of multimedia information retrieval and human-computer interaction in computer vision applications. He was involved in the organization of the major conferences and workshops addressing the computer vision and human-centered  aspects of multimedia information retrieval, among which as a General Co-Chair of the IEEE Automatic Face and Gesture Recognition Conference, FG 2008, ACM International Conference on Image and Video Retrieval (CIVR) 2007 and 2010. He was a general chair of ACM Multimedia 2013 and a program chair of ACM Multimedia 2011 and 2007 and ECCV 2016. He is a program chair of ICCV 2017, ICPR 2020, and the general chair of ICMR 2017. He is the ACM SIGMM vice Chair. He is a fellow of IAPR and a Senior member of ACM and IEEE.

内容提要：

Social perception is the main channel through which human beings access the social world, very much like vision and hearing are channels through which people access the physical world. For a long time, researchers have addressed the computational implementation of vision and hearing in domains like computer vision and speech recognition, but only early attempts have been made to do the same with social perception. We believe social perception to be one of the missing links in the communication between humans and computers, and so in this presentation I will present our recent research in social signals analysis (e.g., head pose, camera-based heart rate estimation, eye gaze). We will concentrate on behavior modeling and recognition, with an emphasis on sensing and understanding users’ interactive actions and intentions, in particular pertaining to dynamic face and body behavior in context dependent situations (task, mood/affect). Perspectives on multisensory observation will be addressed.