Computer Science Seminar: Yan Yan

Time

-

Locations

Stuart Building, Room 111 10 West 31st Street, Chicago, IL 60616

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This event is open to all Illinois Tech students and faculty.

Abstract

Video-scene understanding is used to acquire the information about what, when, where, who, how, and why for the situation that captures the attributes and structure of a scene in videos. Video-scene understanding is a challenge task in computer vision, and is an important step to realize artificial intelligence. Multi-task learning, as one important branch of machine learning, has developed fast during the past decade. Multi-task learning methods aim to simultaneously learn classification or regression models for a set of related tasks. This typically leads to better models as compared to a learner that does not account for task relationships. In this talk, we will investigate a multi-task learning framework for video-scene understanding from low-level to high-level tasks including human pose estimation, action/activity recognition, semantic segmentation, and event detection. Moreover, extracting useful information from videos requires robustness that depends on different sensors. As we know, humans explore the world via different perceptions and multi-modal signals such as audio, visual, and language. Therefore we will also investigate the multi-modal machine learning approach to improve video scene understanding.

Bio

Yan Yan is currently an assistant professor at Texas State University. He was a research fellow at the University of Michigan and at the University of Trento in Italy. He received his Ph.D. in computer science from the University of Trento, and the M.S. degree from Georgia Institute of Technology. He was a visiting scholar with Carnegie Mellon University in 2013 and a visiting research fellow with the Advanced Digital Sciences Center (ADSC), University of Illinois Urbana-Champaign, Singapore in 2015. His research interests include computer vision, machine learning, and multimedia. His research has been funded by NSF, NIST, Cisco, etc. He received the Best Student Paper Award in ICPR 2014, Best Paper Award in ACM Multimedia 2015, and Best Paper Nomination in ACM Multimedia 2018. He has published papers in CVPR, ICCV, ECCV, TPAMI, IJCV, AAAI, IJCAI, and ACM Multimedia. He has been PC members for several major conferences and reviewers for referred journals in computer vision and multimedia. He served as a guest editor in TPAMI, CVIU, and TOMM.

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