Learning With Partial and Noisy Correspondence in Graph Matching

This research addresses a problem in graph matching (a technique for finding correspondences between similar structures), where training data often contains incomplete or incorrect information. The authors propose a dual-expert framework that uses two different mathematical approaches (KB-QAP and L-QAP, which are optimization methods for assignment problems) working together through an align-fuse-refine pipeline to handle both missing keypoints from partial views and errors from mislabeled data.