Visual Correspondences in the Big Data Era
Everyone has some experience of solving jigsaw puzzles. When facing ambiguities of assembling a pair of pieces, a common strategy we use is to look at clues from additional pieces and make decisions among all relevant pieces together. In this talk, I will show how to apply this common practice to develop data-driven algorithms that significantly outperform pair-wise algorithms. I will start with describing a computation framework for the joint inference of correspondences among shape/image collections. Then I will discuss how similar ideas can be utilized to learn visual correspondences, addressing fundamental challenges such as lack of training data and partial similarity.
Speaker
Qixing Huang is an assistant professor at the University of Texas at Austin. He obtained his PhD in Computer Science from Stanford University and his MS and BSin Computer Science from Tsinghua University. He was a research assistant professor at Toyota Technological Institute at Chicago before joining UT Austin.He has also worked at Adobe Research and Google Research, where he developed some of the key technologies for Google Street View. Dr. Huang’s research spans the fields of computer vision, computer graphics, and machine learning. In particular, he is interested in designing new algorithms that process and analyze big geometric data (e.g., 3D shapes/scenes). He is also interested in statistical data analysis, compressive sensing, low-rank matrix recovery, and large-scale optimization, which provides theoretical foundation for his research. Qixing has published extensively at SIGGRAPH, CVPR and ICCV, and has received grants from NSF and various industry gifts.He also received the best paper award at the Symposium on Geometry Processing 2013.