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Special Talk: Fei-Fei Li, Princeton University, “High-level visual recognition: understanding the visual world beyond isolated objects”

April 21, 2009 @ 4:00 pm - 5:00 pm

Abstract: The human visual system is extremely efficient and good at perceiving and understanding the meaning of the visual world. This includes object recognition, scene classification, image segmentation, motion analysis, and many more tasks. Computer vision research has come a long way towards solving these high-level visual recognition tasks. In this talk, I will focus on two topics. First, we discuss a progression of recent research projects in our lab towards high-level image understanding beyond isolated objects. Using a probabilistic learning and recognition framework, we introduce a new algorithm that is capable of performing simultaneous object segmentation, scene annotation and event classification. We show that learning can be done in a fully automatic way by using Flickr images and the highly noisy user tags. Second, we argue that many high-level visual recognition tasks involve the understanding of a pivotal object: humans. In addition to classifying human actions, we present an automatic detection and extraction algorithm for carving out moving humans in arbitrary motions in YouTube videos. We also briefly discuss an activity recognition algorithm based on the understanding of human-object interactions, such as humans playing musical instruments. Finally, if time allows, I will introduce the newly released ImageNet database, a freely accessible image ontology containing millions of human labeled, full-resolution images organized according to the WordNet hierarchy.


Prof. Fei-Fei Li’s main research interest is in vision, particularly high-level visual recognition. In computer vision, Fei-Fei?s interests span from object and natural scene categorization to human activity categorizations in both videos and still images. In human vision, she has studied the interaction of attention and natural scene and object recognition, and decoding the human brain fMRI activities involved in natural scene categorization by using pattern recognition algorithms. Fei-Fei graduated from Princeton University in 1999 with a physics degree. She received PhD in electrical engineering from the California Institute of Technology in 2005. From 2005 to the end of 2006, Fei-Fei was an assistant professor in the Electrical and Computer Engineering Department at University of Illinois Urbana-Champaign. She is currently an Assistant Professor in the Computer Science Department at Princeton University. She also holds courtesy appointments in the Psychology Department and the Neuroscience Program at Princeton. She is a recipient of the 2006 Microsoft Research New Faculty Fellowship. (Fei-Fei publishes under the name L. Fei-Fei.)


April 21, 2009
4:00 pm - 5:00 pm
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