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Generating robust and reliable correspondences across images is a fundamentaltask for a diversity of applications. To capture context at both global andlocal granularity, we propose ASpanFormer, a Transformer-based detector-freematcher that is built on hierarchical attention structure, adopting a novelattention operation which is capable of adjusting attention span in aself-adaptive manner. To achieve this goal, first, flow maps are regressed ineach cross attention phase to locate the center of search region. Next, asampling grid is generated around the center, whose size, instead of beingempirically configured as fixed, is adaptively computed from a pixeluncertainty estimated along with the flow map. Finally, attention is computedacross two images within derived regions, referred to as attention span. Bythese means, we are able to not only maintain long-range dependencies, but alsoenable fine-grained attention among pixels of high relevance that compensatesessential locality and piece-wise smoothness in matching tasks.State-of-the-art accuracy on a wide range of evaluation benchmarks validatesthe strong matching capability of our method.

ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer