AI4IA Colloquium Speaker: Mubarak Shah
Human Action Recognition: Learning with Less Labels and Privacy Preservation
Speaker: Dr. Mubarak Shah, Center for Research in Computer Vision, University of Central Florida
Time: Monday February 6th, 2023, 10:30-11:30 AM (EDT)
Location: Steinman Hall Exhibit Room (ST-124), Grove School of Engineering, The City College of New York, 275 Convent Avenue, New York, NY 10031
Human action recognition is one of the most active areas of research in Computer Vision. Due to deep learning tremendous progress has been made and several high-performance methods have been proposed. This extraordinary success of deep learning methods can be mostly attributed to advancements in supervised learning algorithms and the availability of large-scale labeled datasets. However, constructing large, labeled video datasets for supervised learning tends to be costly and is often infeasible.
In this talk, I will discuss our recent work on human action recognition employing learning with less labels. In particular, I will present our work employing Semi-supervised learning (SSL), Self-supervised learning and Zero-short learning. First, I will present our Uncertainty-aware Pseudo-label Selection (UPS) method for semi-supervised learning, which improves pseudo labeling accuracy by drastically reducing the amount of noise encountered in the training process. Next, I will present self-supervised method, TCLR: Temporal Contrastive Learning for Video Representations, which is a new temporal contrastive learning framework consisting of two novel losses to improve upon existing contrastive self-supervised video representation learning method. Finally, I will present Pairwise-Similarity Zero-shot Action Recognition (PS-ZAR) method. Given a video and a set of action classes, our method predicts a set of confidence scores for each class independently. This allows for the prediction of several semantically distinct classes within one video input.
Advances in action recognition have enabled a wide range of real-world applications, e.g. video surveillance camera, smart shopping systems, elderly person monitor systems. Most of these video understanding applications involve extensive computation, for which a user needs to share the video data to the cloud computation server, where the user also ends up sharing the private visual information like gender, skin color, clothing, background objects etc. Therefore, there is a pressing need for solutions to privacy preserving action recognition. I will end this talk by briefly discuss our recent method SPAct: Self-supervised Privacy Preservation for Action Recognition.
Biography: Dr. Mubarak Shah, the UCF Trustee Chair Professor, is the founding director of Center for Research in Computer Visions at University of Central Florida (UCF). Dr. Shah is a fellow of ACM, IEEE, NAI, IAPR, AAAS, AAIA and SPIE; and a member of Academy of Science, Engineering and Medicine of Florida (ASEMFL). He has published extensively on topics related to visual surveillance, visual tracking, human activity and action recognition, object detection and categorization, shape from shading, geo registration, visual crowd analysis, etc. He has been ACM and IEEE Distinguished Visitor Program speaker and is often invited to present seminars, tutorials and invited talks all over the world. He is a recipient of ACM SIGMM Technical Achievement award; ACM SIGMM Test of Time Honorable Mention Award for his paper in Proceedings of the 14th ACM International Conference on Multimedia, MM 06; International Conference on Pattern Recognition (ICPR) 2020 Best Scientific Paper Award; IEEE Outstanding Engineering Educator Award; Harris Corporation Engineering Achievement Award; an honorable mention for the ICCV 2005 Where Am I? Challenge Problem; 2013 NGA Best Research Poster Presentation; 2nd place in Grand Challenge at the ACM Multimedia 2013 conference; and runner up for the best paper award in ACM Multimedia Conference in 2005 and 2010. At UCF he has received Pegasus Professor Award; University Distinguished Research Award; Faculty Excellence in Mentoring Doctoral Students; Scholarship of Teaching and Learning award; Teaching Incentive Program award; Research Incentive Award.