Animé par Josef Sivic
Building machines that can automatically understand complex visual inputs is one of the central problems in artificial intelligence with applications in autonomous robotics, automatic manufacturing or healthcare. The problem is difficult due to the large variability of the visual world. In this talk, I will present some of our contributions to the recent progress in automatic visual understanding and discuss some of the key open challenges.
I will show examples of our work in developing visual representations learnable with only minimal manual supervision with applications in accurate 3D object pose estimation, visual localization across changing conditions and learning from instructional videos how people manipulate objects.
BIOGRAPHY: Josef Sivic holds a distinguished researcher position at the Institute of Robotics, Informatics and Cybernetics at the Czech Technical University in Prague where he heads the Intelligent Machine Perception project and the recently established ELLIS Unit Prague. He is currently on leave from a senior researcher position at Inria Paris where he remains a close external collaborator of the Willow team. He received the habilitation degree from Ecole Normale Superieure in Paris in 2014 and PhD from the University of Oxford in 2006. After Phd he was a post-doctoral associate at the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. He received the British Machine Vision Association Sullivan Thesis Prize, three test-of-time awards at major computer vision conferences, and an ERC Starting Grant.
ID meeting : 993 0221 3681 Code : 252383