Title: Story Telling in Images: Modeling Visual Hierarchies Within and Across Images Abstract: The human visual system is extremely good at perceiving and understanding the meaning of the visual world. This includes object recognition, scene classification, image segmentation, motion analysis, activity, event understanding, and many more tasks. Pixels in images, and images in the visual world, are not organized in random ways. The human visual system processes information in a hierarchy of visual areas, most likely to achieve efficient and effective processing of the data. In a similar vein, we show that hierarchical representation of the pixel space can be an effective way of modeling increasingly complex visual scenes. We start with a quick review of two past work in basic-level scene classification. Then we show that by putting together over-segmented image regions, objects (and tags) and scenes, we make progress on three fundamental visual recognition tasks (scene classification, object annotation and segmentation) in one coherent, probabilistic model. In an upcoming CVPR paper, we focus on using a hierarchical representation to discover important connectivity between parts of a human body to the object that interacts with the person (e.g. pitching baseball). This hierarchical representation is very effective in providing mutual context to detecting objects and estimating human poses, both are extremely difficult tasks in cluttered visual scenes. And finally, in another upcoming CVPR paper, we show an automatic way of organizing a large number of photographs downloaded from Flickr in a semantically meaningful hierarchy. hierarchy can serve as a useful knowledge structure for visual tasks such as scene classification and annotation. Biography: Prof. Fei-Fei Li's main research interest is in vision, particularly high-level visual recognition. In computer vision, 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 August 2009, Fei-Fei was an assistant professor in the Electrical and Computer Engineering Department at University of Illinois Urbana-Champaign and Computer Science Department at Princeton University, respectively. She is currently an Assistant Professor in the Computer Science Department at Stanford University. Fei-Fei is a recipient of a Microsoft Research New Faculty award and an NSF CAREER award. (Fei-Fei publishes using the name L. Fei-Fei.)