MGNet: Learning Correspondences via Multiple Graphs
Luanyuan Dai Xiaoyu Du Hanwang Zhang Jinhui Tang
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.