Abstract: Visual repetitions are abundant in our surrounding physical world: small image patches tend to reoccur within a natural image, and across different rescaled versions thereof. Similarly, semantic repetitions appear naturally inside an object class within image datasets, as a result of different views and scales of the same object. We studied deviations from these expected repetitions, and demonstrated how these deviations can be exploited to tackle both low-level and high-level vision tasks. These include blind image reconstruction tasks (e.g. dehazing, deblurring), image classification confidence estimation, and more.

Bio: Yuval is a postdoctoral researcher working with Prof. TomerMichaeliat the Technion. His research focuses on the intersection of computer vision and audio processing with Machine learning. He completed his PhD at the Weizmann Institute of Science, where his advisor was Prof. Michal Irani. Previously, he completed his M.Sc. at the Technion, where he was advised by Prof. YoavY. Schechner.

Homepage: http://www.wisdom.weizmann.ac.il/%7Eybahat/