Generate, Analyze, and Refine: Training-Free Sound Source Localization via MLLM Meta-Reasoning
Sound source localization task aims to identify the locations of sound-emitting objects by leveraging correlations between audio and visual modalities. Most existing SSL methods rely on contrastive learning-based feature matching, but lack explicit reasoning and verification, limiting their effectiveness in complex acoustic scenes. Inspired by human meta-cognitive processes, we propose a training-free SSL framework that exploits the intrinsic reasoning capabilities of Multimodal Large Language Models (MLLMs). Our Generation-Analysis-Refinement (GAR) pipeline consists of three stages: Generation produces initial bounding boxes and audio classifications; Analysis quantifies Audio-Visual Con...
| Authors | Subin Park, Jung Uk Kim |
|---|---|
| Primary category | cs.CV |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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