Meet Dr. Jean Ponce

content of images and videos using weak forms of supervision, such as the fact that multiple images contain instances of the same objects, or the textual information available in television or film scripts. I will discuss several instances of this problem, including image cosegmentation, the joint localization and identification of movie characters and their actions, and the assignment of action labels to video frames using temporal ordering constraints. I will present the underlying discriminative clustering model, appropriate relaxations of the combinatorial optimization problems associated with learning its parameters, and efficient algorithms for solving the corresponding convex optimization problems.  I will also present experimental results on standard image benchmarks and feature-length films. I wil conclude with a brief overview of recent work on fully unsupervised object discovery in photographs and videos, learning-based approaches to image restoration, and three-dimensional computer vision.

Bio: 
  Jean Ponce is a Research Director at Inria and a Visiting Researcher at the NYU Center for Data Science, on leave from Ecole Normale Superieure (ENS) / PSL Research University, where he is a Professor, and served as Director of the Computer Science department from 2011 to 2017. Before joining ENS and Inria, Jean Ponce held positions at MIT, Stanford, and the University of Illinois at Urbana-Champaign, where he was a full professor until 2005. Jean Ponce is an IEEE Fellow and a Sr. member of the Institut Universitaire de France. He served as Editor-in-chief for the International Journal of Computer Vision from 2003 to 2008, and chaired the IEEE Conference on Computer Vision and Pattern Recognition in 1997 and 2000, and the European Conference on Computer Vision in 2008. Jean Ponce is the recipient of two US patents, an ERC advanced grant, and the 2016 IEEE CVPR Longuet-Higgins prize. He is also the author of "Computer Vision: A Modern Approach", a textbook translated in Chinese, Japanese, and Russian.