16 citations · 17 across the 3 of their papers we have counts for
4 papers
A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs
Runzhong Wang, Zhigang Hua, Gan Liu +6
Combinatorial Optimization (CO) has been a long-standing challenging research topic featured by its NP-hard nature. Traditionally such problems are approximately solved with heuris…
Combinatorial Learning of Graph Edit Distance via Dynamic Embedding
Runzhong Wang, Tianqi Zhang, Tianshu Yu +2
Graph Edit Distance (GED) is a popular similarity measurement for pairwise graphs and it also refers to the recovery of the edit path from the source graph to the target graph. Tra…
InstaBoost: Boosting Instance Segmentation via Probability Map Guided Copy-Pasting
Hao-Shu Fang, Jianhua Sun, Runzhong Wang +3
Instance segmentation requires a large number of training samples to achieve satisfactory performance and benefits from proper data augmentation. To enlarge the training set and in…
Learning Combinatorial Embedding Networks for Deep Graph Matching
Runzhong Wang, Junchi Yan, Xiaokang Yang
Graph matching refers to finding node correspondence between graphs, such that the corresponding node and edge's affinity can be maximized. In addition with its NP-completeness nat…