Event Location: NSH 1507
Bio: Alex Berg’s research concerns computational visual recognition. He has worked on general object recognition in images, action recognition in video, human pose identification in images, image parsing, face recognition, image search, and machine learning for computer and human vision. He co-organizes the ImageNet Large Scale Visual Recognition Challenge, and organized the first Large-Scale Learning for Vision workshop. He is currently an assistant professor in computer science at UNC Chapel Hill. Prior to that he was on the faculty at Stony Brook University, a research scientist at Columbia University, and research scientist at Yahoo! Research. His PhD at U.C. Berkeley developed a novel approach to deformable template matching. He earned a BA and MA in Mathematics from Johns Hopkins University and learned to race sailboats at SSA in Annapolis. In 2013 his work won the Marr Prize.

Abstract: Recognition techniques in computer vision are beginning to work, making the next question, “What should we recognize?” I will present some work on increasing the label space for recognition toward large numbers of semantic labels embedded in a hierarchy, toward multiple attribute labels, and toward detailed spatial parsing. Predictions of these labels are improving results on problems from face recognition to large scale similar image retrieval and building stronger connections between computer vision and natural language processing. At the same time, this move toward BIGVISION requires that we meet the unavoidable computational challenges in the underlying machine learning problems. I will present some results in each of these directions and try to motivate some of the wide open problems in this area.