Generating robust and reliable correspondences across images is a fundamental
task for a diversity of applications. To capture context at both global and
local granularity, we propose ASpanFormer, a Transformer-based detector-free
matcher that is built on hierarchical attention structure, adopting a novel
attention operation which is capable of adjusting attention span in a
self-adaptive manner. To achieve this goal, first, flow maps are regressed in
each cross attention phase to locate the center of search region. Next, a
sampling grid is generated around the center, whose size, instead of being
empirically configured as fixed, is adaptively computed from a pixel
uncertainty estimated along with the flow map. Finally, attention is computed
across two images within derived regions, referred to as attention span. By
these means, we are able to not only maintain long-range dependencies, but also
enable fine-grained attention among pixels of high relevance that compensates
essential locality and piece-wise smoothness in matching tasks.
State-of-the-art accuracy on a wide range of evaluation benchmarks validates
the strong matching capability of our method.

提出了一种基于 Transformer 的 ASpanFormer 检测器自由匹配器，采用分层注意力结构，能够在全局和本地粒度上捕捉上下文，并具有自适应调整注意跨度的能力，以此来实现在匹配任务中对高相关性像素的细粒度关注。