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Nous avons eu un problème de son pendant les premières 90 secondes de la vidéo, veuillez-nous excuser pour la gêne occasionnée.
Cordelia Schmid holds a M.S. degree in Computer Science from the University of Karlsruhe and a Doctorate, also in Computer Science, from the Institut National Polytechnique de Grenoble (INPG). Her doctoral thesis on "Local Greyvalue Invariants for Image Matching and Retrieval" received the best thesis award from INPG in 1996. She received the Habilitation degree in 2001 for her thesis entitled "From Image Matching to Learning Visual Models". Dr. Schmid was a post-doctoral research assistant in the Robotics Research Group of Oxford University in 1996--1997. Since 1997 she has held a permanent research position at INRIA Grenoble Rhone-Alpes, where she is a research director and directs the INRIA team called LEAR for LEArning and Recognition in Vision. Dr. Schmid is the author of over a hundred technical publications. She has been an Associate Editor for IEEE PAMI (2001--2005) and for IJCV (2004--2012), editor-in-chief for IJCV (2013---), a program chair of IEEE CVPR 2005 and ECCV 2012 as well as a general chair of IEEE CVPR 2015. In 2006 and 2014, she was awarded the Longuet-Higgins prize for fundamental contributions in computer vision that have withstood the test of time. She is a fellow of IEEE. In 2013, she was awarded an ERC advanced grant.
Artificial intelligence (AI) is the intelligence exhibited by machines or software. One of the central problems of AI research is machine perception, i.e., the ability to understand the visual world based on the input from sensors such as cameras. Computer vision is the area which analyzes visual input. A few selected subproblems are facial recognition, object recognition and activity recognition.
In this talk, I will present recent progress in visual understanding. It is for the most part due to design of robust visual representations and learned models capturing the variability of the visual world. Progress has resulted in technology for a variety of applications; I will presented a few examples. This being said, the gap between human and machine performance is still enormous. I will discuss future research necessary to reduce this gap.