Technology Category
Computer Vision: Multi-modal VisionMachine Learning: Learning with ManifoldsSearch and Optimization: Learning to Search
MambaGlue: Fast and Robust Local Feature Matching With Mamba
🤖 AI Summary
Existing deep learning-based image matching methods struggle to balance speed and accuracy in local feature correspondence. To address this, we propose the first end-to-end local feature matching framework built upon the Mamba architecture. Our key contributions are: (1) the pioneering integration of the state-space model Mamba into local matching; (2) a novel MambaAttention mixer enabling joint local-global feature modeling; and (3) a lightweight MLP-based confidence regressor for dynamic selection of high-reliability correspondences. The framework jointly optimizes feature detection, description, and matching in a unified pipeline. Extensive experiments demonstrate state-of-the-art performance across multiple standard benchmarks: inference speed improves by 3.2× over Transformer-based baselines, matching accuracy increases by 12.7%, and the method achieves superior robustness and real-time capability.
Technology Category
Application Category
📝 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
Problem
Research questions and friction points this paper is trying to address.
Deep Learning Image Matching
Speed-Accuracy Trade-off
Local Feature Pairing
Innovation
Methods, ideas, or system contributions that make the work stand out.
MambaAttention
Depth Confidence Score Regressor
MambaGlue
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