GAMES Webinar 2019 – 85期 | 黄其兴（德克萨斯大学奥斯汀分校）
【GAMES Webinar 2019-85期】
报告题目：Map Synchronization: from Object Correspondences to Neural Networks
Maps between geometric objects are deeply interesting. High-quality maps facilitate information propagation and aggregation, leading to numerous applications in geometry reconstruction, synthesis, analysis and prediction. In the deep learning era, the concept of maps naturally generalizes to neural networks between pairs of domains. Despite the importance of maps, map computation remains quite challenging, particularly between dissimilar objects. Map synchronization addresses this challenge by jointly optimizing maps among a collection of relevant objects, which allows us to compute maps between dissimilar objects by composing maps along paths of similar object pairs.
In this talk, we will discuss several recent works on map synchronization: joint map and symmetry synchronization, learning to transformation synchronization and learning a network of neural networks. We will focus on both theoretical connections to optimization, representation theory and graph theory, and practical applications in 3D reconstruction and 3D understanding.
Qixing Huang is an assistant professor of computer science at University of Texas at Austin. He obtained his PhD in Computer Science from Stanford University in 2012. From 2012 to 2014 he was a postdoctoral research scholar at Stanford University. Huang was a research assistant professor at Toyota Technological Institue at Chicago from 2014-2016. He received his MS and BS in Computer Science from Tsinghua University. Huang has also interned at Google Street View, Google Research and Adobe Research.
His research spans computer vision, computer graphics, computational biology, and machine learning. In particular, his recent focus is on developing machine learning algorithms (particularly deep learning) that leverage Big Data to solve core problems in computer vision, computer graphics, and computational biology. He is also interested in statistical data analysis, compressive sensing, low-rank matrix recovery, and large-scale optimization, which provide a theoretical foundation for much of his research. He is an area chair of CVPR 2019 and ICCV 2019.
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