Learning Graph Representations for Intelligent Visual Computing

学习智能视觉计算的图表示

• 批准号: RGPIN-2018-06702
• 负责人: BenHamza, Abdessamad
• 金额: $ 2.04万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=750639
• 关键词: Learning; Graph; Representations; Intelligent; Visual

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.

新兴的智能视觉计算领域旨在利用计算机视觉、几何处理和深度学习方面的最新进展来智能地分析图像、视频和3D形状等视觉数据。深度神经网络在分析视觉数据方面的迅速普及在很大程度上归功于经济实惠的计算硬件、开源软件和对大量训练数据的访问。在这个项目中，我们将研究图信号处理、光谱几何和深度学习如何相互作用，以确定整体视觉数据识别系统的性能，以及如何最好地共同优化这些组件。特别强调将放在3D形状识别的努力，以提供三个相互交织的问题的答案：如何学习鲁棒的形状感知表征；将学习转移到图结构数据的其他任务和模式的最佳方法是什么？学习表征的最佳用途是什么。这个项目的目标是研究设计和开发判别和生成模型的有前途的想法，用于学习深度图表示，并在我们现有的一些工作的基础上沿着这个方向发展。特别是，我们的目标是：(i)通过引入更多来自图信号处理和光谱几何的合理概念，形式化图结构数据的深度神经网络的设计和分析；调查所开发算法的效率和可扩展性；发展图像和三维物体识别的理论严谨和计算可行的方法；探讨已开发的方法对不同视觉数据领域的可转移性；(v)开发已开发解决方案的新的视觉计算应用。除了具有重要的理论意义外，拟议的研究计划的结果将对面向更好地理解视觉数据的最先进技术做出重要贡献，并且还将对各种视觉计算应用产生影响，包括神经成像和推荐系统。该项目不仅将通过推动加拿大已经蓬勃发展的人工智能研究和技术，为加拿大的社会和经济发展做出贡献，而且还将为加拿大的工业、学术界、政府机构和私人组织培养一批熟练人才。