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<p>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: <a href="https://github.com/LiaoYun0x0/FmCFA">https://github.com/LiaoYun0x0/FmCFA</a>.</p>

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FmCFA: a feature matching method for critical feature attention in multimodal images

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年2月24日

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  • Yun Liao,
  • Xuning Wu,
  • Junhui Liu,
  • Peiyu Liu,
  • Zhixuan Pan,
  • Qing Duan

摘要

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.