Selective State-Space Modeling of Correlation Maps for Semantic Correspondence
Keywords: semantic correspondence, state-space models, correlation aggregation, Mamba, feature aggregation, Similarity-aware Selective Scan
TL;DR: We introduce MambaMatcher, which formulates Mamba layers to process the correlation map between image pairs to establish accurate semantic correspondences with improved performance and efficiency.
Abstract: Establishing semantic correspondences between images is a fundamental yet challenging task in computer vision. Traditional feature-metric methods enhance visual features but may miss complex inter-image relationships, while recent correlation-metric approaches attempt to model these relationships but are hindered by high computational costs due to processing 4D correlation maps. We introduce MambaMatcher, a novel method that overcomes these limitations by efficiently modeling high-dimensional correlations using selective state-space models (SSMs), treating multi-level correlation scores as states. By implementing a similarity-aware selective scan mechanism adapted from Mamba’s linear-complexity algorithm, MambaMatcher refines the 4D correlation tensor effectively without compromising feature map resolution or receptive field. Experiments on standard semantic correspondence benchmarks demonstrate that MambaMatcher achieves state-of-the-art performance without relying on large input images or computationally expensive diffusion-based feature extractors, effectively capturing rich inter-image correlations while maintaining computational efficiency.
Primary Area: applications to computer vision, audio, language, and other modalities
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Submission Number: 5072
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