Research Scientist at Google
Full Professor at the University of Edinburgh
My research field is computer vision. My focus is on learning visual models with minimal human supervision, human-machine collaboration, and lifelong learning. I also work on semantic segmentation, object parts, and 3D reconstruction with neural networks.
In the past I have worked on several other computer vision problems, including action recognition, human pose estimation, learning visual attributes, shape matching, contour-based object class detection, specific object recognition, multi-view wide-baseline stereo, tracking in video.
Research group at Google Zurich
My group at Google Research aims at learning visual models from large image datasets where ground-truth location annotations are available for only a small fraction of all images. We develop techniques based on two general ideas. The first is lifelong learning, where the computer continuously learns new models by building on all the knowledge it acquired in the past. This helps bridging the lack of location supervision for the majority of the data, and to build an integrated, coherent body of visual knowledge. The second idea is human-machine collaboration, where a human annotator and a machine learning model work together to annotate a dataset. This helps improving efficiency over annotating it completely manually, and provides the machine valuable labelled examples of what it does not already know.
Check out the publication list!
Vittorio Ferrari is a Research Scientist at Google and a Full Professor at the University of Edinburgh. He received his PhD from ETH Zurich in 2004 and was a post-doctoral researcher at INRIA Grenoble in 2006-2007 and at the University of Oxford in 2007-2008. Between 2008 and 2012 he was an Assistant Professor at ETH Zurich, funded by a Swiss National Science Foundation Professorship grant. In 2012 he received the prestigious ERC Starting Grant, and the best paper award from the European Conference in Computer Vision. He is the author of over 100 technical publications. He regularly serves as an Area Chair for the major computer vision conferences, he was a Program Chair for ECCV 2018 and will be a General Chair for ECCV 2020. He is an Associate Editor of IEEE Pattern Analysis and Machine Intelligence. His current research interests are in learning visual models with minimal human supervision, human-machine collaboration, and semantic segmentation.