MambaMatch: Learning Two-View Correspondences with Selective State Spaces

Xiang Fang, Shihua Zhang, Jiayi Ma

Keywords: Correspondence learning, image matching, feature matching

TL;DR: Correspondences Learning with Selective State Spaces

Abstract: Two-view correspondence learning aims to discern true and false correspondences between image pairs by recognizing their underlying different information. Previous methods either treat the information equally or fail to discard the superfluous information of false correspondences, tending to be invalid in practical scenarios. Therefore, inspired by Mamba's inherent competence of selectivity, we propose MambaMatch as a Mamba-based correspondence filter to selectively mine information from true correspondences and to dispose of the potentially interfering information of false correspondences. Specifically, the selection is achieved by adaptively adjusting model parameters in a high-dimensional latent space, which also avoids attention leakage and implements context compression, ensuring the precise and efficient exploitation of pertinent information. Meanwhile, channel awareness is tailored to serve as a complementary aspect of comprehensive information acquisition. Moreover, we design a novel local-context enhancement module to capture reasonable local context that is crucial for correspondence pruning. Extensive experiments demonstrate that our approach outperforms existing state-of-the-art methods on several visual tasks while saving time and space costs.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 6894