Title: MambaGlue: Fast and Robust Local Feature Matching With Mamba Show Chinese title

Title: MambaGlue：使用Mamba的快速且鲁棒的局部特征匹配

Abstract: In recent years, robust matching methods using deep learning-based approaches have been actively studied and improved in computer vision tasks. However, there remains a persistent demand for both robust and fast matching techniques. To address this, we propose a novel Mamba-based local feature matching approach, called MambaGlue, where Mamba is an emerging state-of-the-art architecture rapidly gaining recognition for its superior speed in both training and inference, and promising performance compared with Transformer architectures. In particular, we propose two modules: a) MambaAttention mixer to simultaneously and selectively understand the local and global context through the Mamba-based self-attention structure and b) deep confidence score regressor, which is a multi-layer perceptron (MLP)-based architecture that evaluates a score indicating how confidently matching predictions correspond to the ground-truth correspondences. Consequently, our MambaGlue achieves a balance between robustness and efficiency in real-world applications. As verified on various public datasets, we demonstrate that our MambaGlue yields a substantial performance improvement over baseline approaches while maintaining fast inference speed. Our code will be available on https://github.com/url-kaist/MambaGlue Show Chinese abstract

Abstract: 近年来，基于深度学习的鲁棒匹配方法在计算机视觉任务中得到了积极研究和改进。 然而，对于鲁棒且快速的匹配技术仍存在持续的需求。 为了解决这一问题，我们提出了一种基于Mamba的局部特征匹配方法，称为MambaGlue，其中Mamba是一种新兴的最先进架构，在训练和推理方面表现出卓越的速度，并与Transformer架构相比展现出有前景的性能。 具体而言，我们提出了两个模块：a) MambaAttention混合器，通过基于Mamba的自注意力结构同时且有选择地理解局部和全局上下文，以及 b) 深度置信度评分回归器，这是一种基于多层感知机（MLP）的架构，用于评估一个分数，该分数表明匹配预测与真实对应关系的匹配程度。 因此，我们的MambaGlue在实际应用中实现了鲁棒性和效率之间的平衡。 在各种公共数据集上的验证表明，我们的MambaGlue在保持快速推理速度的同时，相对于基线方法取得了显著的性能提升。 我们的代码将在 https://github.com/url-kaist/MambaGlue 上提供。