JamMa: Ultra-lightweight Local Feature Matching with Joint Mamba

Existing state-of-the-art feature matchers capture long-range dependencies with Transformers but are hindered by high spatial complexity, leading to demanding training and highlatency inference. Striking a better balance between performance and efficiency remains a challenge in feature matching. Inspired by the linear complexity O(N) of Mamba, we propose an ultra-lightweight Mamba-based matcher, named JamMa, which converges on a single GPU and achieves an impressive performance-efficiency balance in inference. To unlock the potential of Mamba for feature matching, we propose Joint Mamba with a scan-merge strategy named JEGO, which enables: (1) Joint scan of two images to achieve high-frequency mutual interaction, (2) Efficient scan with skip steps to reduce sequence length, (3) Global receptive field, and (4) Omnidirectional feature representation. With the above properties, the JEGO strategy significantly outperforms the scan-merge strategies proposed in VMamba and EVMamba in the feature matching task. Compared to attention-based sparse and semi-dense matchers, JamMa demonstrates a superior balance between performance and efficiency, delivering better performance with less than 50% of the parameters and FLOPs.