UTCS AI Colloquia - Svetlana Lazebnik, "Broad-Coverage Scene Parsing with Object Instances and Occlusion Ordering"
Signup Schedule: http://apps.cs.utexas.edu/talkschedules/cgi/list_events.cgi
Talk Audience: UTCS Faculty, Grads, Undergrads, Other Interested Parties
Host: Kristen Grauman
Talk Abstract: I will present our work on image parsing, or segmenting an image and labeling its regions with semantic categories (e.g., sky, ground, tree, person, etc.). Our aim is to achieve broad coverage across hundreds of object categories in diverse, large-scale datasets of realistic indoor and outdoor scenes. First, I will introduce our baseline nonparametric region-based parsing system that can easily scale to datasets with tens of thousands of images and hundreds of labels. Next, I will describe our approach for combining this region-based system with per-exemplar sliding window detectors to improve parsing performance on small object classes, which achieves state-of-the-art results on several challenging datasets. Finally, I will describe our most recent work that goes beyond per-pixel labels and infers the spatial extent of individual object instances together with their occlusion relationships.
Speaker Bio: Svetlana Lazebnik received her Ph.D. at the University of Illinois at Urbana-Champaign in 2006. From 2007 to 2011, she was an assistant professor of computer science at the University of North Carolina in Chapel Hill, and in 2012 she has returned to the University of Illinois as a faculty member. She is the recipient of an NSF CAREER Award, a Microsoft Research Faculty Fellowship, and a Sloan Foundation Fellowship. She is a member of the DARPA Computer Science Study Group and of the editorial board of the International Journal of Computer Vision. Her research interests focus on scene understanding and modeling the content of large-scale photo collections.
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