PRISM：面向尺度不变图像匹配的渐进依赖最大化方法
PRISM: PRogressive dependency maxImization for Scale-invariant image Matching
AI总结：
PRISM提出渐进依赖最大化方法，通过多尺度剪枝模块和尺度感知动态剪枝注意力联合解决无关特征干扰与尺度差异问题，在多个基准上取得领先匹配精度。
摘要：
图像匹配旨在识别一对图像之间的对应点。目前，无检测器方法凭借其生成稠密匹配和全局感受野的能力，在具有挑战性的场景中表现出色。然而，在整个图像上进行特征交互和提出匹配是不必要的，因为并非所有图像区域都对匹配过程有贡献。在不可匹配区域中进行交互和匹配会引入误差，降低匹配精度和效率。同时，尺度差异问题仍然困扰着现有方法。为了解决上述问题，我们提出了面向尺度不变图像匹配的渐进依赖最大化方法（PRISM），该方法联合剪除不相关的图像块特征并处理尺度差异。为此，我们首先提出了一个多尺度剪枝模块（MPM），通过最大化两个特征集之间的依赖关系来自适应地剪除不相关特征。此外，我们设计了尺度感知动态剪枝注意力（SADPA），通过分层设计聚合来自不同尺度的信息。我们的方法在各种评估基准和下游任务中取得了领先的精度，证实了其优越的匹配性能和泛化能力。代码已公开于 https://github.com/Master-cai/PRISM。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
