# GLMNet: Graph Learning-Matching Networks for Feature Matching

arXiv: https://arxiv.org/abs/1911.07681 · HTML: https://mlsanity.com/paper/1911.07681/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2019.

## Built on by (most cited first, 9 of 9)

- [Deep Graph Matching under Quadratic Constraint](https://mlsanity.com/paper/2103.06643/) (2021)
- [Fusion Moves for Graph Matching](https://mlsanity.com/paper/2101.12085/) (2021)
- [Ensemble Quadratic Assignment Network for Graph Matching](https://mlsanity.com/paper/2403.06457/) (2024)
- [Deep graph matching meets mixed-integer linear programming: Relax at your own risk ?](https://mlsanity.com/paper/2108.00394/) (2021)
- [AMatFormer: Efficient Feature Matching via Anchor Matching Transformer](https://mlsanity.com/paper/2305.19205/) (2023)
- [Multiscale Dynamic Graph Representation for Biometric Recognition with Occlusions](https://mlsanity.com/paper/2307.14617/) (2023)
- [Unsupervised Deep Graph Matching Based on Cycle Consistency](https://mlsanity.com/paper/2307.08930/) (2023)
- [Normalized Matching Transformer](https://mlsanity.com/paper/2503.17715/) (2025)
- [CLAP: Concave Linear APproximation for Quadratic Graph Matching](https://mlsanity.com/paper/2410.17101/) (2024)

## Abstract (arXiv)

Recently, graph convolutional networks (GCNs) have shown great potential for the task of graph matching. It can integrate graph node feature embedding, node-wise affinity learning and matching optimization together in a unified end-to-end model. One important aspect of graph matching is the construction of two matching graphs. However, the matching graphs we feed to existing graph convolutional matching networks are generally fixed and independent of graph matching, which thus are not guaranteed to be optimal for the graph matching task. Also, existing GCN matching method employs several general smoothing-based graph convolutional layers to generate graph node embeddings, in which extensive smoothing convolution operation may dilute the desired discriminatory information of graph nodes. To overcome these issues, we propose a novel Graph Learning-Matching Network (GLMNet) for graph matching problem. GLMNet has three main aspects. (1) It integrates graph learning into graph matching which thus adaptively learn a pair of optimal graphs that best serve graph matching task. (2) It further employs a Laplacian sharpening convolutional module to generate more discriminative node embeddings for graph matching. (3) A new constraint regularized loss is designed for GLMNet training which can encode the desired one-to-one matching constraints in matching optimization. Experiments on two benchmarks demonstrate the effectiveness of GLMNet and advantages of its main modules.