Speaker: Daniel Cohen-Or (Tel Aviv University)
Abstract:
Attention layers play a critical role in generative models. In this talk, I will show that these layers capture rich semantic information, and particularly semantic correspondences between elements within the image and across different images. Through several works, I will show that the rich representations learned by these layers can be leveraged for image manipulation, consistent image generation, and personalization. Additionally, I will discuss the challenges that arise, especially in scenarios involving complex prompts with multiple subjects. Specific issues, such as semantic leakage during the denoising process, can lead to inaccurate representations, resulting in poor generations.
Bio:
Daniel Cohen-Or is a professor in the School of Computer Science. He received his B.Sc. cum laude in both mathematics and computer science (1985), and M.Sc. cum laude in computer science (1986) from Ben-Gurion University, and Ph.D. from the Department of Computer Science (1991) at State University of New York at Stony Brook. He was sitting on the editorial board of a number of international journals, and a member of the program committees of several international conferences. He was the recipient of the Eurographics Outstanding Technical Contributions Award in 2005. In 2013 he received The People’s Republic of China Friendship Award. In 2015 he was named a Thomson Reuters Highly Cited Researcher? He received the ACM SIGGRAPH Computer Graphics Achievement Award in 2018. In 2019 he won The Kadar Family Award for Outstanding Research. In 2020, he received The Eurographics Distinguished Career Award. His research interests are in computer graphics, in particular, synthesis, processing and modelling techniques.