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InfoResearchPeer-reviewed
Learning With Partial and Noisy Correspondence in Graph Matching
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Summary
This paper proposes the first unified framework for graph matching that handles both partial correspondence, caused by viewpoint occlusions, and noisy correspondence, caused by annotation errors. The method uses a dual-expert design combining Koopmans-Beckmann and Lawler's quadratic assignment formulations (KB-QAP and L-QAP) in an align-fuse-refine pipeline. Experiments on four widely used graph matching datasets show it outperforms 17 competitive baselines in both scenarios.