2026-09-23T20:01:36.188Z
- title:
G-Mamba: Sparse Graph-Guided Mamba for Audio-Visual Speech Enhancement→ SG-Mamba: Sparse Graph-Guided Mamba for Audio-Visual Speech Enhancement
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SG-Mamba: Sparse Graph-Guided Mamba for Audio-Visual Speech Enhancement
Lightweight audio-visual speech enhancement (AVSE) models face a critical trade-off between computational efficiency and cross-modal alignment accuracy. While simple concatenation lacks relational expressiveness, dense cross-attention incurs computational overhead and is prone to unreliable cross-modal correspondence under strong acoustic interference. We propose Sparse Graph-Guided Mamba (SG-Mamba), a lightweight AVSE framework that integrates a sparse heterogeneous graph with a linear-complexity Mamba backbone. The graph explicitly models modality-specific relations through content-adaptive
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-16T02:00:09.000Z
First collected: 2026-09-20T08:20:57.646Z. This is not the publication date.
Observed changes
AIIC observation times, not verified publisher revision times. Up to eight recent revisions.