Image matching aims at identifying corresponding points between a pair of images. Currently, detector-free methods have shown impressive performance in challenging scenarios, thanks to their capability of generating dense matches and global receptive field. However, performing feature interaction and proposing matches across the entire image is unnecessary, because not all image regions contribute to the matching process. Interacting and matching in unmatchable areas can introduce errors, reducing matching accuracy and efficiency. Meanwhile, the scale discrepancy issue still troubles existing methods. To address above issues, we propose PRogressive dependency maxImization for Scale-invariant image Matching (PRISM), which jointly prunes irrelevant patch features and tackles the scale discrepancy. To do this, we firstly present a Multi-scale Pruning Module (MPM) to adaptively prune irrelevant features by maximizing the dependency between the two feature sets. Moreover, we design the Scale-Aware Dynamic Pruning Attention (SADPA) to aggregate information from different scales via a hierarchical design. Our method's superior matching performance and generalization capability are confirmed by leading accuracy across various evaluation benchmarks and downstream tasks. The code is publicly available at https://github.com/Master-cai/PRISM.

本研究解决了图像匹配中的特征互动多余和尺度差异问题。提出的PRISM方法通过引入多尺度修剪模块(MPM)和尺度感知动态修剪注意力(SADPA)，有效提高了不相关特征的修剪和不同尺度信息的聚合。实验结果显示，该方法在多个评估基准和下游任务上表现出色，具有较强的匹配性能和泛化能力。

PRISM：用于尺度不变图像匹配的渐进依赖最大化