14 citations · 16 across the 4 of their papers we have counts for
4 papers
M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering
Jiaxin Lu, Zetian Jiang, Tianzhe Wang +1
Existing graph matching methods typically assume that there are similar structures between graphs and they are matchable. However, these assumptions do not align with real-world ap…
Learning Universe Model for Partial Matching Networks over Multiple Graphs
Zetian Jiang, Jiaxin Lu, Tianzhe Wang +1
We consider the general setting for partial matching of two or multiple graphs, in the sense that not necessarily all the nodes in one graph can find their correspondences in anoth…
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching
Chang Liu, Zetian Jiang, Runzhong Wang +3
Graph matching (GM) has been a building block in various areas including computer vision and pattern recognition. Despite recent impressive progress, existing deep GM methods often…
Rethinking Sampling in 3D Point Cloud Generative Adversarial Networks
He Wang, Zetian Jiang, Li Yi +3
In this paper, we examine the long-neglected yet important effects of point sampling patterns in point cloud GANs. Through extensive experiments, we show that sampling-insensitive…