אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Scene understanding requires the extraction and representation of scene components together with their individual properties, as well relations and interactions between them. In current computer vision, there has been considerable progress in recognizing scene components (people, objects, parts), but the problem of recovering scene structure is still largely open. I will describe a model that performs scene interpretation by an iterative process, combining bottom-up and top-down networks, interacting through a symmetric bi-directional communication between them. The model extracts and recognizes scene components with their selected properties and relations, and uses them to describe and understand the image. Short Bio: Shimon Ullman is the Samy and Ruth Cohn Professor of Computer Science at the Weizmann Institute of Science, and the head of the Weizmann Institute AI Center. Prior to this position, he was a Professor at the Brain and Cognitive Science and the AI Laboratory at MIT. His areas of research combine computer and human vision, human cognition, and brain modeling. He obtained his B.Sc. in Mathematics, Physics and Biology, at the Hebrew University of Jerusalem, and Ph.D. in Electrical Engineering and Computer Sciences, at the Artificial Intelligence Laboratory in the Massachusetts Institute of Technology. He is a recipient of the 2008 David. E. Rumelhart Prize in human cognition, the 2014 Emet Prize for Art, Science and Culture, the 2015 Israel Prize in Computer Science, and the 2019 IEEE Azriel Rosenfeld Award for lifetime achievement in computer vision. He is a member of the Israeli Academy of Sciences and Humanities, and the American Academy of Arts and Sciences.