MGNet: Learning Correspondences via Multiple Graphs
This addresses the challenge of improving correspondence matching in computer vision, which is incremental as it builds on existing graph neural network approaches by integrating multiple graph types.
The paper tackles the problem of learning correspondences from sparse, unevenly distributed initial sets with low inlier rates by proposing MGNet, which combines multiple complementary graphs to capture potential relationships, and it outperforms state-of-the-art methods in various visual tasks.
Learning correspondences aims to find correct correspondences (inliers) from the initial correspondence set with an uneven correspondence distribution and a low inlier rate, which can be regarded as graph data. Recent advances usually use graph neural networks (GNNs) to build a single type of graph or simply stack local graphs into the global one to complete the task. But they ignore the complementary relationship between different types of graphs, which can effectively capture potential relationships among sparse correspondences. To address this problem, we propose MGNet to effectively combine multiple complementary graphs. To obtain information integrating implicit and explicit local graphs, we construct local graphs from implicit and explicit aspects and combine them effectively, which is used to build a global graph. Moreover, we propose Graph~Soft~Degree~Attention (GSDA) to make full use of all sparse correspondence information at once in the global graph, which can capture and amplify discriminative features. Extensive experiments demonstrate that MGNet outperforms state-of-the-art methods in different visual tasks. The code is provided in https://github.com/DAILUANYUAN/MGNet-2024AAAI.