Sharing Knowledge for Large Scale Visual Recognition
This talk overviews my research activities in computer vision, pattern recognition and multimedia for understanding big visual data. I will focus on two models for “sharing” prior and contextual knowledge for solving large scale visual recognition problems. In the first part of the talk, I'll show that images that are very difficult to recognize on their own may become more clear in the context of a neighborhood of related images with similar social-network metadata. Our model uses image metadata non-parametrically to generate neighborhoods of related images, then uses a deep neural network to blend visual information from the image and its neighbors. In the second part of the talk, I'll present our recent work on knowledge transfer for scene-specific motion prediction. When given a single frame of a video, humans can not only interpret the content of the scene, but also they are able to forecast the near future. This ability is driven by their rich prior knowledge about the visual world, both in terms of the dynamics of moving agents, as well as the semantic of the scene. We exploit the interplay between these two key elements to predict scene-specific motion patterns on a novel large dataset collected from UAV.
Lamberto Ballan is a senior postdoctoral researcher at Stanford University and University of Florence, Italy, supported by a prestigious Marie Curie Fellowship from the European Commission. He will be an assistant professor (tenure track) of computer science at the University of Padova starting in Fall 2017. He received the Laurea and Ph.D. degrees in computer engineering in 2006 and 2011, both from the University of Florence. He was also a visiting scholar at the Signal and Image Processing department at Telecom Paristech, France, in 2010. His research interests lie at the boundary of computer vision and multimedia, specifically focused on exploiting big data for visual recognition problems. The primary aim of his current research is on designing learning algorithms that make the most effective use of prior and contextual knowledge in presence of sparse and noisy labels.