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BVISS: Action localization without spatiotemporal supervision
Friday 24, November, 2017 @ 16:00 - 17:00
Dr Cees Snoek – Faculty of Science, University of Amsterdam
Understanding what activity is happening where and when in video content is crucial for video computing, communication and intelligence. In the literature, the common tactic for action localization is to learn a deep classifier on hard to obtain spatiotemporal annotations and to apply it at test time on an exhaustive set of spatiotemporal candidate locations. Annotating the spatiotemporal extent of an action in training video is not only cumbersome, tedious, and error prone, it also does not scale beyond a hand full of action categories. In this presentation, I will highlight recent work from my team at the University of Amsterdam in addressing the challenging problem of action localization in video without the need for spatiotemporal supervision. We consider three possible solution paths: 1) the first relies on intuitive user-interaction with points, 2) the second infers the relevant spatiotemporal location from an action class label, and finally, 3) the third derives a spatiotemporal action location from off-the-shelf object detectors and text corpora only. I will discuss the benefit and drawbacks of these three solutions on common action localization datasets, compare with alternatives depending on spatiotemporal supervision, and highlight the potential for future work.
Cees Snoek received the M.Sc. degree in business information systems in 2000 and the Ph.D. degree in computer science in 2005, both from the University of Amsterdam, The Netherlands. He is currently a director of the QUVA Lab, the joint research lab of Qualcomm and the University of Amsterdam, on deep learning and computer vision. He is also a principal engineer/manager at Qualcomm Research Netherlands and an associate professor at the University of Amsterdam. His research interests focus on video and image recognition. He is recipient of a Veni Talent Award, a Fulbright Junior Scholarship, a Vidi Talent Award, and The Netherlands Prize for Computer Science Research.