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.