For most of its existence, Visual Computing has been primarily focused on algorithms, with data treated largely as an afterthought. Only recently, with the advances in AI, did our field start to truly appreciate the singularly crucial role played by data, but even now we might still be underestimating it. In this talk, I will begin with some historical examples illustrating the importance of large visual data for visual analysis and synthesis. I will then share some of our recent work demonstrating the power of very simple algorithms when used with the right data, including scene analysis, model interpolation, and visual data attribution.
Alexei (Alyosha) Efros joined UC Berkeley in 2013. Prior to that, he was for a decade on the faculty of Carnegie Mellon University, and has also been affiliated with École Normale Supérieure/INRIA and University of Oxford. His research is in the area of computer vision and computer graphics, especially at the intersection of the two. He is particularly interested in using data-driven techniques to tackle problems where large quantities of unlabeled visual data are readily available. Efros received his PhD in 2003 from UC Berkeley. He is a recipient of CVPR Best Paper Award (2006), Sloan Fellowship (2008), Guggenheim Fellowship (2008), Okawa Grant (2008), SIGGRAPH Significant New Researcher Award (2010), three PAMI Helmholtz Test-of-Time Prizes (1999,2003,2005), the ACM Prize in Computing (2016), Diane McEntyre Award for Excellence in Teaching Computer Science (2019), Jim and Donna Gray Award for Excellence in Undergraduate Teaching of Computer Science (2023), and PAMI Thomas S. Huang Memorial Prize (2023).