Professor Alexei A. Efros from UC Berkeley gives a lecture as part of the Munich AI Lectures.
For most of Computer Vision’s existence, the focus has been solidly on algorithms and models, with data treated largely as an afterthought. Only recently did the discipline finally begin to 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 in human and computer vision. I will then share some of our recent work demonstrating the power of very simple algorithms when used with the right data, including visual in-context learning 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.
You are also invited to the following reception (8-9 pm) after the lecture.
The Munich AI Lectures are a joint initiative of baiosphere, Bavarian Academy of Sciences and Humanities (BAdW), Helmholtz Munich, Ludwig-Maximilians-Universität München (LMU), Technische Universität München (TUM), AI-HUB@LMU, ELLIS Chapter Munich, Konrad Zuse School of Excellence in Reliable AI (relAI), Munich Center for Machine Learning (MCML), Munich Data Science Institute (MDSI) at TUM and Munich Institute of Robotics and Machine Intelligence (MIRMI), coordinated by the Bavarian AI Agency. The aim is to attract renowned AI researchers to Munich.