LAUSR

Multimodal image feature matching is a critical technique in computer vision. However, many current methods rely on extensive attention interactions, which can lead to the inclusion of irrelevant information from non-critical regions, introducing noise and consuming unnecessary computational resources. In contrast, focusing attention on the most relevant regions (information-rich areas) can significantly improve the subsequent matching phase. To address this, we propose a feature matching method called FmCFA, which emphasizes critical feature attention interactions for multimodal images. We introduce a novel Critical Feature Attention (CFA) mechanism that prioritizes attention interactions on the key regions of the multimodal images. This strategy enhances focus on important features while minimizing attention to non-essential ones, thereby improving matching efficiency and accuracy, and reducing computational cost. Additionally, we introduce the CFa-block, built upon CF-Attention, to facilitate coarse matching. The CFa-block strengthens the information exchange between key features across different modalities. Extensive experiments demonstrate that FmCFA achieves exceptional performance across multiple multimodal image datasets. The code is publicly available at: https://github.com/LiaoYun0x0/FmCFA.