SAMOT: State-Aware Step Modulation and Optimal Transport Matching for Audio-Visual Instance Segmentation

Abstract

Audio-Visual Instance Segmentation (AVIS) aims to simultaneously classify, segment, and track sounding objects within video sequences. Unlike Audio-Visual Semantic Segmentation (AVS), AVIS involves instance-level modeling across longer video sequences, introducing two key challenges: (1) complex modality-state changes disrupt long-range modeling, and (2) substantial structural and distributional discrepancies between modalities hinder precise instance-level association. Existing methods rely on fixed-step Transformers and recursive Mamba models, lacking adaptability to modality-state changes. In addition, methods performing implicit matching ignore the inherent distributional inconsistencies. To address these issues, we propose a framework with Adaptive Dynamic Step Modulation (ADSM) and Optimal Transport-based Matching Modulation (OT-MM). ADSM adaptively modulates Mamba step sizes using temporal variation, cross-modal discrepancy, and historical context, balancing rapid response to modality-state changes with stable long-range modeling. OT-MM explicitly formulates instance-level cross-modal matching as an entropy-regularized optimal transport problem solved via log-domain Sinkhorn iterations, and further enforces distribution-level coherence with an MMD regularizer. Extensive experiments demonstrate state-of-the-art performance on the AVIS benchmark (+3.76 FSLA, +2.75 HOTA, +2.58 mAP), verified through comprehensive qualitative visualizations. The code and model are available at https://github.com/happylife-pk/SAMOT.