  Vision transformers (ViTs) have recently been used for visual matching beyond
object detection and segmentation. However, the original grid dividing strategy
of ViTs neglects the spatial information of the keypoints, limiting the
sensitivity to local information. Therefore, we propose QueryTrans (Query
Transformer), which adopts a cross-attention module and keypoints-based center
crop strategy for better spatial information extraction. We further integrate
the graph attention module and devise a transformer-based graph matching
approach GMTR (Graph Matching TRansformers) whereby the combinatorial nature of
GM is addressed by a graph transformer neural GM solver. On standard GM
benchmarks, GMTR shows competitive performance against the SOTA frameworks.
Specifically, on Pascal VOC, GMTR achieves $\mathbf{83.6\%}$ accuracy,
$\mathbf{0.9\%}$ higher than the SOTA framework. On Spair-71k, GMTR shows great
potential and outperforms most of the previous works. Meanwhile, on Pascal VOC,
QueryTrans improves the accuracy of NGMv2 from $80.1\%$ to $\mathbf{83.3\%}$,
and BBGM from $79.0\%$ to $\mathbf{84.5\%}$. On Spair-71k, QueryTrans improves
NGMv2 from $80.6\%$ to $\mathbf{82.5\%}$, and BBGM from $82.1\%$ to
$\mathbf{83.9\%}$. Source code will be made publicly available.
