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Deep Anomaly Detection for Computer Vision Applications

Time / Place:

⏱️ 10/7 (Thur.) 20:45-21:10 at Online Track 2

Abstract:

Recently, there have been rapid advances of anomaly detection based on the deep learning framework. In this talk, I will first give a brief overview of different deep anomaly detection approaches, such as supervised, semi-supervised, weakly supervised, and unsupervised approaches with various formulations.

Then, I will present our recent works on how we develop the deep anomaly detection techniques to various real-world computer vision problems, including defect detection from images, face anti-spoofing, video surveillance, and abnormal human action detection.

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Biography:

賴尚宏
  • 賴尚宏 Shang-Hong Lai Website: http://www.cs.nthu.edu.tw/~lai
  • Microsoft AI R&D Center, Taiwan / Principal Researcher

  • Shang-Hong Lai received the Ph.D. degree from University of Florida, Gainesville, USA. He worked at Siemens Corporate Research in Princeton, New Jersey, USA, as a member of technical staff during 1995-1999.

    Since 1999, he joined the Department of Computer Science, National Tsing Hua University, Taiwan, where he is now a professor there. Since the summer of 2018, Dr. Lai has been on leave from NTHU to join Microsoft AI R&D Center, Taiwan.

    He is currently a principal researcher at Microsoft AI R&D Center and leads a science team focusing on computer vision research related to face related applications. Dr. Lai’s research interests are mainly focused on computer vision, image processing, and machine learning.

    He has authored more than 300 papers published in refereed international journals and conferences in these areas. In addition, he has been awarded around 30 patents on his researches on computer vision.

    He has involved in the organization for a number of international conferences in computer vision and related areas, such as ICCV, CVPR, ACCV, ICIP, etc.

    Furthermore, he has served as an associate editor for Journal of Signal Processing Systems and Pattern Recognition.

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