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Found 2 papers, 1 papers with code
Adaptive Edge Attention for Graph Matching with Outliers
To explore the potential of edges, EAGM learns edge attention on the assignment graph to 1) reveal the impact of each edge on graph matching, as well as 2) adjust the learning of edge representations adaptively.
Ranked #10 on
Graph Matching
on PASCAL VOC
(matching accuracy metric)
Graph Attribute Aggregation Network with Progressive Margin Folding
Graph convolutional neural networks (GCNNs) have been attracting increasing research attention due to its great potential in inference over graph structures.
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