Learning Graph Representations for Intelligent Visual Computing

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The newly emerging field of intelligent visual computing seeks to leverage recent advances in computer vision, geometry processing and deep learning to intelligently analyze visual data such as images, videos and 3D shapes. The swift uptake of deep neural networks in analyzing visual data is largely attributed to a combination of affordable computing hardware, open source software, and access to large amounts of training data.******In this program, we will study how graph signal processing, spectral geometry and deep learning interact to determine overall visual data recognition system performance, and how best to jointly optimize these components. A particular emphasis will be placed on 3D shape recognition in an effort to provide answers to three intertwined problems: how to learn robust shape-aware representations; what is the best way to transfer learning to other tasks and modalities for graph-structured data; and what are the best uses for the learned representations.******The objective of this program is to research promising ideas in the design and development of discriminative and generative models for learning deep graph representations, and also to build on some of our existing work along this direction. In particular, we aim to: (i) Formalize both the design and analysis of deep neural networks for graph-structured data by introducing more sound concepts from graph signal processing and spectral geometry; (ii) Investigate the efficiency and scalability of the developed algorithms; (iii) Develop theoretically rigorous and computationally feasible methodologies for image and 3D object recognition; (iv) Explore the transferability of the developed approaches to different visual data domains; and (v) Exploit novel visual computing applications of the developed solutions.******In addition to having significant theoretical implications, the outcome of the proposed research program will be important contributions to the state-of-the-art techniques geared toward a better understanding of visual data, and will also have an impact on a variety of visual computing applications, including neuroimaging and recommender systems. Not only would this program contribute to the social and economic development of Canada by boosting its already flourishing AI research and technology, but it would also contribute toward the training of a number of skilled personnel available to Canadian industry, academia, government agencies, and private organizations.