This talk will be about few-shot instance segmentation in images -- a basic computer vision problem arising in many applications with access to only a few examples of target object classes due to, e.g., their rarity. Our main research accomplishments in this area will be presented, including: explicit modeling and discovery of latent object parts shared across object classes; data uncertainty map estimation for each instance segmentation and data uncertainty based regularization of our few-shot learning; and model uncertainty estimation with an efficient approximation based on the probit function. Our contributions produce statistically significant performance gains over the state of the art on the benchmark COCO dataset.
Sinisa Todorovic is Professor in the School of Electrical Engineering and Computer Science at ¾«¶«Ó°ÊÓ(OSU). He joined OSU in 2008, after three years of conducting postdoctoral research in the Beckman Institute at University of Illinois Urbana-Champaign (UIUC). He received his Ph.D. degree in electrical and computer engineering at University of Florida. Todorovic conducts research in computer vision with focus on object and human-activity recognition. He has served as a co-Program Chair of IEEE International Conference on Automatic Face and Gesture Recognition FG 2015, co-organized a number of tutorials and workshops on stochastic image grammars at top vision conferences, and currently serves as Editor-in-Chief of the Image and Vision Computing journal.  His research has been funded by a number of federal agencies, including NSF and DARPA, as well as the IT industry. He received Jack Neubauer Best Paper Award from IEEE Transactions on Vehicular Technology in 2014, and the ¾«¶«Ó°ÊÓ State College of Engineering Research Collaboration Award in 2016.