Professor and former director, Department of Computer Science (on leave at Inria since Fall 2017), Ecole normale superieure. Distinguished Visiting Faculty, NYU (Fall 2017-). IEEE Fellow (2003). Recipient of an ERC Sr. Grant (2011) and the CVPR and ICML test-of-time awards (2016 and 2019). Sr. Editor-in-Chief, International Journal of Computer Vision (2019-).
Topics of interest
Computer vision, image processing, machine learning, robotics
Project in Prairie
Jean Ponce will address scale and supervision issues in vision, visually guided robotics, the NLP/vision interface, biological image restoration, cultural heritage preservation, and “blue-sky” collaborations with industry in vision and robotics. He will participate in the PSL AI graduate school, and develop a reference annual AI summer school.
Today’s computer vision technology is quite good at identifying animals, people, or natural and man-made objects in cluttered images and videos. But it relies on a humongous amount of manual annotation to learn the corresponding visual models. The vision systems of tomorrow will have to continuously learn from data with a much weaker level of human supervision, to adapt to new users for digital assistants or new routes and driving conditions for autonomous cars, and truly leverage the billions of images available on the Internet. This change of paradigm is necessary for the successful large-scale deployment of computer vision technology, and it is a central scientific challenge for our field.